Jáchym Kolář

Yang Liu
email: yangl@hlt.utdallas.edu

Elizabeth Shriberg

Jáchym Kolář

Speaker Adaptation of Language and Prosodic Models for Automatic Dialog Act Segmentation of Speech

Keywords: Spoken language understanding, Dialog act segmentation, Speaker adaptation, Prosody modeling, Language modeling

Speaker-dependent modeling has a long history in speech recognition, but has received less attention in speech understanding. This study explores speaker-specific modeling for the task of automatic segmentation of speech into dialog acts (DAs), using a linear combination of speaker-dependent and speaker-independent language and prosodic models. Data come from 20 frequent speakers in the ICSI meeting corpus; adaptation data per speaker ranges from 5k to 115k words. We compare performance for both reference transcripts and automatic speech recognition output. We find that: (1) speaker adaptation in this domain results both in a significant overall improvement and in improvements for many individual speakers, (2) the magnitude of improvement for individual speakers does not depend on the amount of adaptation data, and (3) language and prosodic models differ both in degree of improvement, and in relative benefit for specific DA classes. These results suggest important future directions for speaker-specific modeling in spoken language understanding tasks.

Introduction

The general idea of model adaptation to a particular talker has successfully been used in the cepstral domain for speech recognition, for example by [START_REF] Gauvain | Maximum a posteriori estimation for multivariate Gaussian mixture obsevation of Markov chains[END_REF] and [START_REF] Gales | Maximum likelihood linear transformations for HMM-based speech recognition[END_REF]. However, less is known about speaker adaptation for spoken language understanding. This paper explores the question of speaker adaptation of generic models for a language understanding task. We focus on speaker-specific modeling for one spoken language understanding task, automatic dialog act (DA) segmentation of speech. This task is important since standard automatic speech recognition (ASR) systems output only a raw stream of words, leaving out important structural information such as locations of sentence or DA boundaries. Such locations are overt in standard text via punctuation and capitalization, but are "hidden" in speech. As shown by a number of studies, the absence of sentence or DA boundaries in speech transcripts causes difficulties for both humans and computers.

Effects on human sentence processing were studied by [START_REF] Jones | Measuring the readability of automatic speech-to-text transcripts[END_REF], who demonstrated that sentence breaks are critical for readability of speech transcripts. Moreover, a lack of sentence segmentation can make the meaning of some utterances ambiguous. To take an extreme case, if an automatic speech recognizer outputs the stream of words "no rooms are available", it is not clear what was saidwhether it was "No rooms are available." or "No. Rooms are available." In this example, the two possible interpretations have completely opposite meaning. Such cases are relatively rare, but other forms of ambiguity can be much more frequent.

Lack of linguistic unit boundaries also causes sig- nificant problems for automatic processing. Many natural language processing (NLP) techniques (e.g., parsing, automatic summarization, information extraction, machine translation) are typically trained on well-formatted input, such as text, and fail when dealing with unstructured streams of words. For instance, [START_REF] Furui | Speechto-text and speech-to-speech summarization of spontaneous speech[END_REF] reported that speech summarization improved when sentence boundaries were provided. In the area of parsing, [START_REF] Kahn | Parsing conversational speech using enhanced segmentation[END_REF] achieved a significant improvement in parsing performance when using a more accurate sentence boundary detection system. Furthermore, [START_REF] Matusov | Improving speech translation with automatic boundary prediction[END_REF] showed that the use of automatically-detected sentence boundaries is beneficial for machine translation.

State-of-the-art approaches to DA segmentation typically use both lexical and prosodic information. Most prior work on this task has focused on identifying effective features or on developing advanced models. Such work has almost exclusively trained aggregate models, representing data pooled over speakers.

In this work, we investigate whether the speakers differ enough from each other in the production of DA boundaries to merit speaker-dependent modeling for this task. We perform speaker adaptation for both language and prosodic models, using a speech corpus of multiparty meetings. The meeting domain is chosen for several reasons. First, we are interested in spontaneous speech, since modeling of idiosyncratic lexical patterns for DA segmentation would not be meaningful for corpora of read speech. We also expect that idiosyncratic prosodic patterns are best seen in spontaneous speech. Second, as in any study of adaptation, it is essential to have enough data to adapt the general model to the specific one. Thus in our case the target domain should have speakers with plenty of speech data, and ideally data from different conversations for purposes of generalization.

For these reasons, we use data from a corpus comprising a series of naturally-occurring meetings. This corpus, like many real-world meetings, has recurring participants, presenting the opportunity for adapting models to the individual talkers. Furthermore, in this corpus, as in other meeting applications, the speakers are known beforehand and are recorded on separate channels. This allows us to focus on the question of inherent contributions from speaker-adaptive modeling, rather than confound results with the issue of speaker separation or recognition.

We ask several questions about speaker variation in lexical and prosodic patterns associated with DA boundaries. First, we ask whether speakers differ enough from overall (speaker-independent) models to benefit from model adaptation using a relatively small amount of their speech. Second, we explore whether the effectiveness of adaptation is correlated with the data amount available for adaptation. If this is not found to be the case, then it would suggest that speakers differ inherently in how well they are characterized by generic models. Third, we investigate whether adaptation performance is dependent on different DA types (for example statements versus questions). Finally, we compare speaker adaptation results for language modeling versus prosodic modeling.

The remainder of this paper is organized as follows. Section 2 surveys related work. Section 3 describes our language and prosodic models for DA segmentation, and the speaker adaptation approach. Section 4 and Section 5 present our experimental setup and discuss the experiment results. Section 6 provides a summary and conclusions.

Related Work

General (speaker-independent) methods for automatic detection of linguistic unit boundaries in speech have been studied quite extensively in the past decades. Several different approaches utilizing both textual (lexical or syntactic) and acoustic (prosodic) information have been proposed. The proposed techniques include hidden Markov models (HMMs) [START_REF] Shriberg | Prosody-based automatic segmentation of speech into sentences and topics[END_REF][START_REF] Kim | A combined punctuation generation and speech recognition system and its performance enhancement using prosody[END_REF], multilayer perceptrons [START_REF] Warnke | Integrated dialog act segmentation and classification using prosodic features and language models[END_REF][START_REF] Srivastava | Sentence boundary detection in Arabic speech[END_REF], maximum entropy [START_REF] Huang | Maximum entropy model for punctuation annotation from speech[END_REF][START_REF] Liu | Comparing and combining generative and posterior probability models: Some advances in sentence boundary detection in speech[END_REF], conditional random fields [START_REF] Liu | Using conditional random fields for sentence boundary detection in speech[END_REF][START_REF] Zimmermann | Joint segmentation and classification of dialog acts using conditional random fields[END_REF], support vector machines [START_REF] Akita | Sentence boundary detection of spontaneous Japanese using statistical language model and support vector machines[END_REF][START_REF] Magimai-Doss | Entropy based classifier combination for sentence segmentation[END_REF], and adaptive boosting [START_REF] Zimmermann | The ICSI+ multilingual sentence segmentation system[END_REF]Kolář et al., 2006b). Syntactic information has been used in [START_REF] Roark | Reranking for sentence boundary detection in conversational speech[END_REF][START_REF] Favre | Efficient sentence segmentation using syntactic features[END_REF]. Domain adaptation for sentence boundary detection has been studied by [START_REF] Cuendet | Model adaptation for sentence segmentation from speech[END_REF]. However, basically no attention has been paid to speaker adaptation of lexical or prosodic models.

For related work, we should mention papers focusing on speaker-dependent modeling for general LM adaptation in speech recognition. [START_REF] Besling | Language model speaker adaptation[END_REF] improved an automatic speech dictation system by speaker LM adaptation based 2 on the LM fill-up method. [START_REF] Akita | Language model adaptation based on PLSA of topics and speakers[END_REF] showed improved recognition performance using LM speaker adaptation by scaling the n-gram probabilities with the unigram probabilities estimated via probabilistic latent semantic analysis. [START_REF] Tur | Unsupervised language model adaptation for meeting recognition[END_REF] demonstrated that unsupervised within-speaker LM adaptation significantly reduced word error rate in meeting speech recognition.

Even less is known about speaker-specific variation in prosodic patterns, beyond basic F 0 normalization used by [START_REF] Shriberg | Prosody-based automatic segmentation of speech into sentences and topics[END_REF]. Studies in speech synthesis and speaker recognition have used prosodic variation successfully, but to our best knowledge, modeling stylistic prosodic variability for sentence or DA boundary detection has been mentioned only anecdotally in the literature [START_REF] Ostendorf | A hierarchical stochastic model for automatic prediction of prosodic boundary locations[END_REF]Hirst and Cristo, 1998).

We have already presented preliminary results of this work in two conference papers (Kolář et al., 2006a[START_REF] Kolář | Speaker adaptation of language models for automatic dialog act segmentation of meetings[END_REF]. Unlike the two earlier papers, this paper contains more results, analysis, and discussion. It also compares the two adaptation methods (language and prosodic) and investigates effects based on DA types.

Modeling and Features

For a given word sequence W = w 1 w 2 ...w i ...w n , the task of DA segmentation is to determine which interword boundaries correspond to a DA boundary. We label each interword boundary as either a within-unit boundary or a boundary between two DAs. For example, in the utterance "yes we should be done by noon", there are two DAs: "yes" and "we should be done by noon". Here we describe language and prosodic models used in this work, as well as our approach to their speaker adaptation.

Language Models (LM)

We use a hidden event LM [START_REF] Stolcke | Automatic detection of sentence boundaries and disfluencies based on recognized words[END_REF] to automatically detect DA boundaries in the unstructured word sequence. This approach based on the hidden Markov model (HMM) framework has been widely used for sentence and DA segmentation and generally achieves performance comparable to other approaches. The hidden event LM describes the joint distribution of words and DA boundaries, P LM (W, B), where B is the DA boundary sequence corresponding to the word sequence W . In training, the DA boundary is explicitly included as a token in the vocabulary, and an n-gram LM is trained based on the word and DA boundary sequences in the training data. In all the experiments reported herein, we used trigram LMs with modified Kneser-Ney smoothing [START_REF] Kneser | Improved backing-off for M-gram language modeling[END_REF] in the SRILM toolkit [START_REF] Stolcke | SRILM -An extensible language modeling toolkit[END_REF].

During testing, a forward-backward algorithm is used to compute the posterior probability P (B i |W ) of a boundary B i at position i given a word sequence W . Then a decision is made to select B i with the highest posterior probability P (B i |W ) at each individual boundary. This approach minimizes the expected per-boundary classification error rate.

Prosodic Features and Models

Our prosodic features are designed to reflect breaks in temporal, intonational, or energy contours. Note that we are interested in speaker differences after normalizing for a speaker's inherent pitch range. The features are extracted from the word-level and phone-level time alignment information obtained from an automatic speech recognizer [START_REF] Shriberg | Prosody-based automatic segmentation of speech into sentences and topics[END_REF]. This approach computes features directly from the signal, without the need for any human labeling of prosodic events. The prosodic features can be grouped into four broad feature classes: pause, pitch, duration, and energy. Features are computed in regions local to each candidate boundary to be classified. That is, we compute prosodic features in a window around each of the arrows in a word sequence w 1 ↓ w 2 ↓ ...w i ↓ ... ↓ w n , where the word sequence is from either the reference transcripts or the ASR output. Features reflect prosodic information either before, after, or across the word boundary. To capture local prosodic dynamics, we also use features associated with the previous and the following boundaries.

The pause features capture raw durations of pauses between adjacent words. The duration features include phone-normalized durations of vowels, final rhymes, and words; normalization statistics are generated from the entire training set. We did not use raw duration features, since they correlate with lexical features that should be modeled in a language model. Certain frequent DAs (especially backchannels) have small sets of words, so raw durations may capture those words rather than prosody. Our pitch features include the minimum, maximum, and mean values of F 0 , F 0 slopes, and the differences and ratios of values across word boundaries. The pitch features are extracted from F 0 contours stylized by a piece-wise linear function [START_REF] Sönmez | Modeling dynamic prosodic variation for speaker verification[END_REF]. The energy features are represented by the maximal, minimal, and mean frame-level RMS values, using both raw and perchannel normalized values.

For prosody modeling, we used decision tree classifiers [START_REF] Breiman | Classification and Regression Trees[END_REF] because they have been found in past work to yield good results, and because they offer the advantage of interpretability with respect to individual features. Because DA boundaries are much less frequent than non-DA boundaries, we had to cope with the problem of data skew. To overcome this problem and to decrease classifier variance, we use a combination of ensemble sampling with bagging [START_REF] Liu | A study in machine learning from imbalanced data for sentence boundary detection in speech[END_REF]. Since the trees were trained on bagged ensembles downsampled to equal class priors, when applying the classifiers on (the imbalanced) test data, the output posteriors, P (B|AP ) (where AP is acoustic prosodic features), were adjusted to take into account the original class priors.

We first developed a large set of candidate prosodic features, and then performed feature selection to identify a small set of useful features, in two steps. First, for each of the broad prosodic feature categories, we selected those features with a feature usage statistic (measured by the number of times the feature is used in the decision trees, see Analysis 1 in Section 5.2 for more details) higher than a predefined threshold. Then using these features, we performed leave-one-out feature selection and removed a feature if its deletion did not yield any performance loss. The selection was performed in speaker-independent fashion. We also tried a speaker-dependent selection, but it did not yield overall performance improvement. The final prosodic feature set contained 32 features. Among them, 16 correspond to duration, 10 to pitch, 3 to energy, and 3 to pause. Importance of individual feature groups in terms of feature usage will be discussed in Analysis 1 in Section 5.2.

Speaker Adaptation Approach

To adapt the generic speaker-independent models to a particular speaker, we employ an interpolation approach. The same approach is used for both language and prosodic model adaptation. The speaker-adapted (SA) result is obtained from a linear combination of posterior probabilities from the speaker-independent model SI, which is trained from all available data, and a speaker-dependent model SD, which is trained using only data from the target talker, that is

P SA (B|X; λ) = λP SI (B|X)+(1-λ)P SD (B|X)(1)
where X denotes either the observed word context or prosodic feature vector, depending on the adapted model type, P SI (B|X) and P SD (B|X) denote speaker-independent and speaker-dependent posteriors, and λ is a weighting factor that is empirically optimized on development data. Note that the SD data is already contained in the SI data for training. Therefore, for the case of LM adaptation, the interpolation does not help reduce outof-vocabulary rate. It rather gives a larger weight to n-grams observed in the data corresponding to a particular speaker and is expected to better suit this speaker. Analogous assumptions also motivate prosodic model adaptation.

Data and Experimental Setup

Speech Database and Dialog Act Annotation

All experiments presented in this paper are evaluated using the ICSI meeting corpus [START_REF] Janin | The ICSI meeting corpus[END_REF]. The database contains approximately 72 hours of multichannel conversational speech data and associated human transcripts, manually annotated for DAs [START_REF] Dhillon | Meeting recorder project: Dialog act labeling guide[END_REF]. The DA annotation distinguishes five broad DA classes:

• Backchannels (B) -Responses to a speaker who has the floor as that speaker is talking; such responses generally do not elicit feedback Spk 1: "We'll start with the presentation -" Spk 2: "Uh-huh." Spk 1: " -and then have lunch."

• Disruptions (D) -Utterances that are indecipherable, abandoned, or interrupted "Tell me about the -| Do you hear me? "

• Floor grabbers/holders (F) -DAs pertaining to mechanisms of grabbing or maintaining the floor Spk 1: "I am sure about it. | I can -" Spk 2: "Well I -| It's not so easy."

• Questions (Q) -Interrogative DAs "Are you happy?"

• Statements (S) -Standard declarative DAs "She will arrive tomorrow."

A small number of DAs were not assigned into any of the above presented classes. These were mostly pre-or post-meeting chatter or utterances that were mumbled or could not be understood for some reason.

Reference and ASR Transcripts

Two different test conditions are used for evaluation: reference transcripts (REF) and automatic speech recognition output (ASR). Recognition results were obtained using the state-of-the-art SRI ASR system, originally developed for the conversational telephone speech domain [START_REF] Stolcke | Recent innovations in speech-to-text transcription at SRI-ICSI-UW[END_REF]. The recognizer was trained using no acoustic data or transcripts from the analyzed meeting corpus. To represent a fully automatic system, we also used automatic speech/nonspeech segmentation. Word error rates for this difficult data are still quite high; the ASR system performed at 38.2% (on the whole corpus). To generate the "reference" DA boundaries for the ASR words, we aligned the reference setup to the recognition output with the constraint that two aligned words could not occur further apart than a fixed time threshold (0.5 sec).

Experimental Setup

For our experiments, we selected the top 20 speakers in terms of each speaker's total number of words. Each speaker's data was split into a training set (∼70% of data) and a test set (∼30%), with the caveat that a speaker's recording in any particular meeting appeared in only one of the two sets. Because of data sparsity, especially for the less frequent speakers, we did not use a separate development set, but rather jackknifed the test set in our experiments. In this approach, one half of speaker's test data is used to tune weights for the other half, and vice versa.

The total training set for speaker-independent models (comprising the training portions of the 20 analyzed speakers, as well as all data from 32 other less-frequent speakers) contained 567k words. The total test set contained 204k words for reference test conditions and 180k for ASR test conditions. Data set sizes for individual speakers are shown in Table 1. Speaker identity is described using the official corpus speaker IDs. The first letter of the ID denotes the gender of the speaker ("f" or "m"); the second letter indicates whether the speaker is a native ("e") or nonnative ("n") speaker of English. The speakers displayed in the table are sorted according to the total numbers of words they have in the corpus. The size of training sets available for training of the speaker-dependent models ranges from 5.2k to 115.2k words. Note that the test set sizes for REF and ASR conditions differ since usually there are fewer words in ASR outputs than in the corresponding reference.

Evaluation Metric

For performance evaluation, we use classification error rate, called Boundary Error Rate (BER) since it refers to the number of interword boundaries in the test set [START_REF] Shriberg | Prosody-based automatic segmentation of speech into sentences and topics[END_REF]. It is defined as

BER = Ins + Del N W [%] (2)
where Ins denotes the number of false DA boundary insertions, Del the number of misses, and N W the number of words in the test set. We also present chance error for every experiment -the error rate

Results and Discussion

Results for Language Model Adaptation

Table 2 shows a comparison of DA segmentation performance for the baseline speaker-independent LM and speaker-adapted LMs for individual speakers, using reference transcripts. The results indicate that performance improved for 17 of 20 speakers. Note that it is possible for the SA to perform worse than the SI model, because weights are estimated on fairly small amounts of data that are separate from the data on which the model is tested. The degree of the improvement varies across particular speakers. For 12 talkers, the improvement was statistically significant at p < 0.05 using the Sign test.

Table 3 reports the corresponding results for the ASR conditions. The results show that 18 speakers improved by LM speaker adaptation. Of the two speakers that did not improve, one also did not improve for the REF condition. The improvement was statistically significant at p < 0.05 for 12 of the 18 improved speakers.

An important finding from these results is that for both test conditions, the relative error reduction achieved by speaker adaptation is not correlated with the amount of adaptation data. This finding suggests that speakers differ inherently in how similar they are to the generic speaker-independent LM. Some talkers probably differ more and thus show more gain, even with less data available for model adaptation. 1) across all speakers is 0.49, so the SI and SD components are approximately balanced in the speaker-adapted LMs.

An overall comparison of performance of baseline

To better understand the effects of speaker adaptation on model performance, we also analyzed the results broken down by DA types as described in Section 4.1. The results for individual DA types are shown for both test conditions in Table 5. Along with SI and SA error rates, the table shows numbers of words in each DA group and relative error changes by speaker adaptation. Negative relative numbers indicate error rate reduction, and positive numbers mean increased errors.

In REF conditions, LM speaker adaptation benefited all DA types. The largest relative improvement was achieved for B. Improvements were much smaller for the other types (S, Q, F, and D). From these results, we can infer that B is the class that has the most speaker-idiosyncratic lexical patterns associated with DA boundaries. The results for ASR show some differences in comparison with REF, especially for B. Unlike REF, speaker adaptation resulted in an error increase for B in ASR conditions. The discrepancy may be explained by the largely different numbers of backchannels in REF and ASR transcripts; backchannels are often missed by the ASR system. We notice that the number of backchannels in ASR conditions is 6 times lower than that in REF. We hypothesize that those backchannels that were recognized correctly are also the ones that are less likely to be speaker specific since the frequent backchannels have higher probabilities in the LM used for speech recognition. The highest error reduction by speaker adaptation in ASR conditions was achieved for S, followed by F, Q, and D. The improvements for these types are slightly less than those in the REF condition, possibly due to ASR errors.

Results for Prosodic Model Adaptation

Table 6 compares performance of SI and SA prosodic models for individual speakers in REF conditions. The results indicate that the SA model is better than the SI model for 7 of the 20 speakers. For 6 other speakers, SI and SA achieved identical results because λ was estimated as 0. Using the Sign test, 4 of the 7 improved speakers showed improvements significant at p < 0.05 or better; one speaker (fn002) was marginally significant at p < 0.10.

Although only some speakers show these improvements (while some others show rather poor results from SA modeling), the finding is important. If a speaker shows significantly improved results using a model trained on far less data than the SI model, this suggests that the speaker's prosodic marking of DA boundaries differs from that of the SI model. That a number of speakers do not benefit from SA modeling is consistent with their being well described by the SI model. That is, there are most likely some consistent ways that people behave prosodically, but for some speakers who deviate more from these norms, speaker-dependent modeling is an important direction to investigate for further improvements.

Table 7 presents results for ASR conditions. The results indicate that 15 speakers improved by prosodic model adaptation. Although this is more speakers than improved for the REF condition, the magnitudes of improvement were relatively smaller for the ASR than for the REF condition. Note that 6 of the 7 speakers who improved in REF also improved in ASR conditions. Also note that the speakers who improved in ASR but not in REF have on average relatively smaller improvements than those who improved in both conditions. Similar to our findings for LM adaptation, the error reduction of the SA model with respect to SI varies across speakers in a manner uncorrelated with amount of adaptation data. Using the Sign test, only one speaker (mn007) showed a gain significant at p < 0.05 in ASR. One other speaker (mn005) showed a marginally significant gain (p < 0.10). The variance between REF and ASR may be explained, at least in part, by the differences in the test sets caused by ASR errors. For example, the ASR data contain a smaller number of backchannels, which are more difficult for speakerspecific prosodic modeling (as shown below in Analysis 2 ). However, the results indicate that there is more variance in speaker-dependent prosodic modeling than in speaker-dependent language modeling. Furthermore, we compare the average values of λ between speaker-adapted language and prosodic models. While the average λ across all speakers for LM adaptation is 0.49, it is 0.67 for prosodic model adaptation. Thus, the SD component (1-λ in Eq 1) is less represented in the SA prosodic models than in the SA language models. The overall results summed over all 20 speakers are shown for both test conditions in Table 8. In REF tests, the overall best performance is from the SA model. The improvement of SA over SI in reference conditions is statistically significant at p < 0.001 using the Sign test. Given the absolute difference in BER, the relatively high level of significance may seem a little surprising, but there exist two explanatory reasons. First, the overall test set is quite large (204k words). Second, if the SD information does not benefit a particular speaker, it often gets zero weight in the interpolation model. Thus, both SA and SI show identical output for many unimproved speakers, which decreases variance between the overall outputs and consequently supports statistical significance in the Sign test. In ASR conditions, the SA model was also the best performing one. The superiority of SA over SI was significant at p < 0.05. The relative gain by prosodic model speaker adaptation in ASR conditions was higher than that achieved in REF conditions.

Analysis 1: Prosodic feature usage

To better understand the results, we analyzed relative prosodic feature usage for the speakers who improved in REF conditions. REF was chosen rather than ASR to focus on the aspect of the speaker-dependent prosodic characteristics rather than analyzing effects of speech recognition errors. The metric "feature usage" [START_REF] Shriberg | Prosody-based automatic segmentation of speech into sentences and topics[END_REF] reflects the number of times a feature is queried in a tree, weighted by the number of samples it affects at each node. Total feature usage within a tree sums to 1. The statistics here are based on averaging results over multiple trees generated in bagging. For this analysis, the prosodic features were grouped into five nonoverlapping groups: pause at the boundary in question, duration, pitch, energy, and "near pause" (pause durations at the preceding and the following interword boundaries). We compare the SD feature usage distribution with the SI distributions. In addition, we divide the speakers into two categories: native English speakers and non-native speakers. The relative feature usage statistics from this analysis are shown in Figure 1.

The three native speakers show very similar usage to each other and to the SI model. However, as we saw earlier, SA improves their results. This suggests that even when general feature usage patterns for a talker are similar to those of the SI model, specific features and/or feature thresholds may still be better modeled by training on the specific speaker. Given that this analysis is based on only three native speakers (their SA model improved over SI), it is possible that not all native speakers show the same pattern. This is a question for further research on a larger data set.

Feature usage for nonnative speakers, on the other hand, looks quite different. Speakers differ from each other, as well as from the SI pattern. Although more research is needed before drawing final conclusions, this finding is nevertheless consistent with stylistic differences between nonnative speakers and an overall SI model, in prosodic marking of DA boundaries. An obvious next question would be whether improvement depends on native language, proficiency in English, or degree of perceived accent. The sample of nonnative speakers is too small to examine these questions. In our preliminary analysis, we found that among the three native German speakers in our data set, all highly proficient in English, one speaker improved from individual modeling while two others did not. Of the three Spanish speakers, all moderately proficient, two improved and one did not. Further studies are still needed to examine other factors.

Analysis 2: DA types, prosody and LM

We performed analysis of prosodic adaptation benefits for individual DA types. The results are displayed in Table 9, for both REF and ASR conditions. In REF conditions, the highest improvement from speaker adaptation was achieved for D, followed by Q and S. On the other hand, speaker adaptation resulted in worse results for F, and especially for B. The results for ASR conditions are quite similar, with slightly better improvement for most DA types. Speaker adaptation benefited D, S, and Q, but hurt F and B. The effects of prosodic adaptation on B and F are almost opposite to those of LM adaptation. For example, in REF conditions, B had the highest improvement by LM adaptation, but the highest performance drop by prosodic adaptation. Similarly, F shows improvement for LM but not for prosody. Thus, we conclude that, overall, prosodic model speaker adaptation has different improvement patterns than LM speaker adaptation.

Another obvious question is whether the speakers who benefit from prosodic model adaptation The number of improved speakers in common may seem high, but it approximately corresponds to the chance agreement based on the counts of improved speakers in both sets (5.95 speakers). Similarly, of the 15 speakers who improved by prosodic adaptation in ASR conditions, 13 improved also by LM adaptation, while the chance agreement is 13.5 speakers. These numbers indicate that there was no apparent correlation between speakers' idiosyncrasy in prosodic and lexical patterns associated with DA boundaries.

Summary and Conclusions

We have investigated speaker-specific language and prosodic modeling for automatic DA segmentation in multiparty meetings. The method was evaluated on 20 frequent speakers with amounts of data available for speaker-dependent modeling ranging from 5k to 115k words. In the first set of experiments, we explored speaker adaptation of hidden event language models. Improvements were found for 17 of the 20 speakers using reference transcripts, and for 18 of the 20 speakers using automatic transcripts. It was not expected that all speakers may improve because some speakers may be close to the SI model. We also measured overall results, summed over all 20 speakers. These results showed that we achieved a statistically significant improvement over the baseline LM for both test conditions.

In the second set of experiments for speaker adaptation of prosody model, we observed improved results for 7 of the 20 speakers in reference conditions, and for 15 of the 20 speakers in ASR conditions. In the ASR conditions, we observed a higher number of improved speakers, but the improvements were relatively smaller than those in the REF conditions. Also note that the speakers who improved in ASR but not in REF have on average relatively smaller improvements than those who improved in both conditions. The variance between REF and ASR may be explained in part by the differences in the test sets caused by ASR errors. However, the results indicate that there is more variance in speaker-dependent prosodic modeling than in language modeling. Overall results, summed over all 20 speakers, indicate modest yet statistically significant improvement by prosodic model adaptation for both test conditions. The relative overall improvement was smaller than that achieved by LM adaptation. Prosodic feature analysis, while preliminary given the number of speakers, suggests that nonnative speakers may differ from native speakers in overall feature usage patterns associated with DA boundaries.

For both types of speaker adaptation, improvements were achieved even for some talkers who had only a relatively small amount of data available for adaptation. In addition, the relative error reduction achieved by speaker adaptation was not correlated with the amount of adaptation data. This finding suggests that speakers differ inherently in how similar they are to the generic models. Some talkers probably differ more and thus show more gain, 10 even with less data. We also found that the agreement between speakers who improved by LM adaptation and those who improved by prosodic adaptation is not higher than chance. Thus, there is no apparent correlation between speakers' idiosyncrasy in prosodic and lexical patterns associated with DA boundaries. Further analysis revealed that LM and prosodic speaker adaptation differ in the DA types that they benefit. For example, while LM adaptation benefits backchannels and floor grabbers/holders, prosodic adaptation hurts them. We conclude that speaker adaptation aids both prosody-and LM-based DA segmentation, and that future work should investigate the potential of speaker-specific modeling for other tasks in spoken language understanding. The techniques for model adaptation to speaker behavior may be extremely helpful for various speech-based applications. Important areas for future extensions of our work include the integration of lexical with prosodic or even multimodal information in a single model, investigating clustering of speakers similar in behavior for greater model robustness, and exploration of unsupervised adaptation approaches.

Figure 1 :

 1 Figure 1: Relative usage of prosodic feature groups for native (top) and nonnative (bottom) speakers who improved from SA in REF conditions

Table 1 :

 1 Data set sizes for individual speakers. ID=Speaker ID, Train=Training set size, Test-Ref=Test size for REF, Test-ASR=Test size for ASR. All data sizes are presented as numbers of words.

	No.	ID	Train Test-Ref Test-ASR	No.	ID	Train Test-Ref Test-ASR
	1.	me013 115.2k	51.2k	43.4k	11. mn052 10.7k	3.8k	3.5k
	2.	me011	50.6k	24.8k	22.9k	12. mn021	9.6k	4.1k	4.1k
	3.	fe008	50.6k	22.6k	19.5k	13.	me003	9.3k	3.6k	3.2k
	4.	fe016	32.0k	15.4k	13.9k	14. mn005	7.7k	3.1k	3.0k
	5.	mn015 31.9k	14.7k	13.7k	15.	me045	8.1k	2.4k	2.1k
	6.	me018	31.8k	14.7k	13.3k	16.	me025	7.7k	2.4k	1.6k
	7.	me010	26.1k	12.6k	11.3k	17.	me006	6.9k	1.5k	1.3k
	8.	mn007 27.2k	10.1k	8.4k	18.	me026	5.2k	2.5k	2.3k
	9.	mn017 21.0k	7.1k	6.0k	19.	me012	5.3k	2.1k	1.9k
	10. mn082 13.3k	4.2k	3.7k	20.	fn002	5.9k	1.5k	1.4k

Table 2 :

 2 DA segmentation error rates for speaker-independent (SI) and speaker-adapted (SA) language models for individual speakers in REF conditions [BER %]. Better result for each speaker is shown in boldface, * indicates that the improvement of SA over SI is significant by the Sign test at p < 0.05.

	ID	Chance	SI	SA	ID	Chance	SI	SA
	me013*	13.66%	6.75%	6.52%	mn052	16.53%	7.33%	7.28%
	me011*	16.09%	7.40%	7.25%	mn021*	13.06%	6.68%	5.65%
	fe008*	13.79%	7.51%	7.16%	me003	13.36%	8.78%	8.56%
	fe016*	14.54%	7.35%	7.18%	mn005*	12.99%	7.83%	6.92%
	mn015*	14.41%	8.05%	7.80%	me045	22.31%	8.90%	8.90%
	me018*	17.22%	6.64%	6.45%	me025	18.07%	8.06%	7.85%
	me010*	14.11%	7.24%	6.84%	me006	19.46%	9.53%	9.47%
	mn007*	20.52%	7.59%	7.31%	me026	11.28%	5.80% 5.80%
	mn017*	15.05%	7.02%	6.44%	me012*	16.21%	6.85%	6.29%
	mn082	11.17%	6.33%	6.21%	fn002	19.71% 10.92% 11.33%

Table 3 :

 3 DA segmentation error rates of speaker-independent and speaker-adapted (SA) language models for individual speakers in ASR conditions [BER %]. Better result for each speaker is shown in boldface, * indicates that the improvement of SA over SI is significant by the Sign test at p < 0.05.

	ID	Chance	SI	SA	ID	Chance	SI	SA
	me013*	12.16%	8.29%	8.18%	mn052*	14.03%	10.87% 10.17%
	me011*	14.77%	8.81%	8.51%	mn021*	10.70%	8.20%	7.73%
	fe008*	13.32%	9.19%	8.89%	me003	12.82%	9.36%	9.33%
	fe016	13.72%	8.42%	8.31%	mn005*	6.81%	11.47% 10.42%
	mn015*	12.69%	10.16%	9.84%	me045	16.21% 11.08% 11.27%
	me018*	15.09%	8.12%	7.90%	me025	16.89% 14.36% 14.36%
	me010*	13.28%	8.39%	7.91%	me006*	16.06%	10.94% 10.40%
	mn007*	14.80%	11.28% 10.78%	me026	10.09%	7.35%	6.88%
	mn017*	11.80%	8.92%	7.84%	me012	13.39%	8.68%	8.31%
	mn082	12.31%	10.37% 10.10%	fn002	13.83%	13.40% 12.89%
	achieved by classifying every word boundary into
	the class with the highest prior probability (which
	is "non-DA boundary" in our case). Chance perfor-
	mance reflects a speaker's relative rate of various
	DA types. For instance, a high chance error rate
	typically correlates with a speaker who produces
	many short DAs, such as backchannels.

Table 4 :

 4 Overall DA segmentation error rates of speakerindependent (SI) and speaker-adapted (SA) language models inREF and ASR conditions [BER %]

	Model	REF	ASR
	Chance 15.02% 13.19%
	SI	7.30%	9.06%
	SA	6.99% 8.76%
	speaker-independent and speaker-adapted LMs is
	presented in Table 4. In total, the test set contains
	204k words for REF and 180k words for ASR con-
	ditions. The results show that for both conditions,
	speaker-adapted LMs outperform the baseline. The
	overall improvements by LM speaker adaptation
	for both conditions are statistically significant at
	p < 10 -15 , using the Sign test. Also note that
	the average value of the interpolation weight λ (in
	Equation

Table 5 :

 5 DA segmentation error rates for SI and SA language models by DA type. B -Backchannel, D -Disruption, F -Floor grabber/holder, Q -Question, S -Statement; N W -Number of test words for the DA type, REL -Relative error change after adaptation (negative numbers indicate error reduction, positive numbers error increase).

			REF			ASR
	DA	N W	SI	SA	REL	N W	SI	SA	REL
	B	3.6k	9.19%	7.70% -16.16%	0.6k	18.40% 18.91% +2.78%
	D	20.9k 12.50% 12.33% -1.38%	17.6k 14.11% 13.98% -0.89%
	F	4.3k	38.00% 36.81% -3.13%	3.0k	43.45% 42.61% -1.93%
	Q	15.4k	6.30%	6.02%	-4.43%	13.7k	8.82%	8.69%	-1.49%
	S	157.3k 5.75%	5.47%	-4.82%	143.3k 7.65%	7.31%	-4.40%

Table 6 :

 6 DA segmentation error rates for SI and SA prosodic models in REF conditions [BER %]. Better result for each speaker is shown in boldface, * indicates that the improvement of SA over SI is significant at p < 0.05 using the Sign test.

	ID	Chance	SI	SA	ID	Chance	SI	SA
	me013	13.66%	8.36%	8.39%	mn052	16.53% 8.29%	8.32%
	me011*	16.09%	6.61%	6.41%	mn021	13.06% 8.01%	8.08%
	fe008	13.79%	8.53%	8.55%	me003	13.36% 5.83% 5.83%
	fe016*	14.54%	9.62%	9.52%	mn005*	12.99%	7.73%	7.18%
	mn015	14.41%	7.99%	7.96%	me045	22.31% 7.20%	7.29%
	me018	17.22%	7.74%	7.74%	me025	18.07% 8.32% 8.32%
	me010	14.11%	8.30%	8.20%	me006	19.46% 9.86%	9.99%
	mn007*	20.52% 10.71% 10.19%	me026	11.28% 7.94% 7.94%
	mn017	15.05%	8.03%	8.03%	me012	16.21% 8.66% 8.66%
	mn082	11.17%	9.00%	9.02%	fn002	19.71%	9.79%	9.32%

Table 7 :

 7 DA segmentation error rates for SI and SA prosodic models in ASR conditions [BER %]. Better result for each speaker is shown in boldface, * indicates that the improvement of SA over SI is significant at p < 0.05 using the Sign test.

	ID	Chance	SI	SA	ID	Chance	SI	SA
	me013	12.16%	8.41%	8.37%	mn052	14.03%	8.96%	8.70%
	me011	14.77%	7.01%	6.77%	mn021	10.70%	7.56%	7.56%
	fe008	13.32%	9.04%	8.90%	me003	12.82%	6.40%	6.27%
	fe016	13.72%	9.40%	9.41%	mn005	6.81%	6.78%	5.90%
	mn015	12.69%	8.09%	7.96%	me045	16.21%	7.93%	7.89%
	me018	15.09%	8.16%	8.18%	me025	16.89% 11.14% 10.83%
	me010	13.28%	8.62%	8.30%	me006	16.06% 10.94% 10.63%
	mn007*	14.80% 10.74% 10.01%	me026	10.09%	7.91%	7.61%
	mn017	11.80%	8.04%	8.04%	me012	13.39%	8.99%	8.99%
	mn082	12.31%	8.03%	8.00%	fn002	13.83%	9.74%	9.46%

Table 8

 8

	: Overall DA segmentation error rates for speaker-
	independent (SI) and speaker-adapted (SA) prosodic models
	in REF and ASR conditions [BER %]
	Model	REF	ASR
	Chance 15.02% 13.19%
	SI	8.25%	8.41%
	SA	8.19% 8.26%

Table 9 :

 9 DA segmentation error rates for SI and SA prosodic models by DA type. B -Backchannel, D -Disruption, F -Floor grabber/holder, Q -Question, S -Statement; N W -Number of test words for the DA type, REL -Relative error change after adaptation (negative numbers indicate error reduction, positive numbers error increase).

			REF			ASR
	DA	N W	SI	SA	REL	N W	SI	SA	REL
	B	3.6k	6.69%	7.20% +7.53%	0.6k	19.93% 21.12% +5.98%
	D	20.9k	9.96%	9.78%	-1.87%	17.6k	9.72%	9.33%	-3.97%
	F	4.3k	37.81% 38.40% +1.54%	3.0k	44.92% 45.42% +1.12%
	Q	15.4k	6.32%	6.23%	-1.34%	13.7k	6.38%	6.27%	-1.83%
	S	157.3k 7.43%	7.36%	-0.88%	143.3k 7.63%	7.49%	-1.87%

Acknowledgments

This work was supported by the Ministry of Education of the Czech Republic under projects 1M0567 and 2C06020 at UWB Pilsen, and the NSF grants IIS-0544682 at SRI International and IIS-0845484 at UT Dallas. The views are those of the authors and do not reflect the views of the funding agencies.


S Makris

G Chryssolouris

Customer's behavior modeling for manufacturing planning

Keywords: PROBABILISTIC MODELS, MASS CUSTOMIZATION, DECISION SUPPORT

This paper deals with a customer driven manufacturing planning approach. Manufacturers have adopted modern communication technologies for the information flow related to customers' orders. However, there is still high uncertainty in the information provided. This work introduces a model for estimating the probability that once a customer has received a potential delivery date for a product, whether he will actually place the order. In this instance the manufacturing resources should be committed to this order. The Bayesian networks method is adopted and an automotive industrial case study is discussed.

Introduction

This work discusses a method of evaluating the probability that a customer, under a certain delivery time and price and given a set of factors, submits an order for a product. This is based on the Bayesian networks method (Schay2007) and is applied to the automotive industry. With appropriate modification, the method is applicable to industry in general.

The automotive industry for a long time has been following the "push" model; build products, based on one or more forecasts and eventually creating stocks of these products. The dealers' role was important since they were obliged to acquire a minimum quantity of the vehicles produced, and therefore, push the product to the market. The customer's element was actually not the driving factor in the automotive production. The automotive industry is turning from the "push" model to a customer driven model, which necessitates that a method be developed to quantify the customers' likely responses to what the automotive is offering [START_REF] Michalos | Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach[END_REF]. Today's research on mass customization [START_REF] Mourtzis | Supply chain modeling and control for producing highly customized products[END_REF], Chryssolouris et al 2008) does consider the fluctuating demand. This fluctuating demand is based on assumptions and forecasts the production of many products' variants apparently without meeting the customers' real needs. Involving the customer in the manufacturing process is more than just forecasting his possible preferences, but it actually calls for his precise requirements to be met. In the mass customization approach, which aims to simultaneously target scores of individual customers by offering them product variations, tailored specifically to fit their individual needs, understanding customer behaviour under growing market stratification conditions is critical (Pasek et. al. 2008).

A growing interest over the past decade in the mass customization approach underscores the importance of the individual consumer choices, in terms of both their structure and parameters (Tseng and Piller 2003).

There is a risk involved in the order promising process and the method proposed in this paper, has been developed in order to face it. This risk is caused by the fact that a dealer provides the customer with a due date for his order. As soon as a due date is given to the customer, the production plant needs to be in position to accomplish the order in this date.

Therefore, when planning the resources usage for a period of time, it is necessary to know, if this order should be considered or not. Consequently, it is essential to have an estimation of what the possibility will be as to whether the customer will actually place the order or he will withdraw it.

In the case of his withdrawing the order, the planning should be adjusted. So, a more accurate estimation of a customer's likely decision, will be an important aid for the preparation of a more accurate plan for the production that will be subject to a minimum amount of changes.

In order for this risk to be eliminated, the paper discusses a method of quantifying the likelihood that a customer will actually place his order and therefore, help the production planner 3 to establish a more accurate plan. A typical case, in which such resource locking situation occurs, is that of the automotive when a customer, at dealership, asks for a delivery date that would eventually lead to locking the plants and supply chain's resources in order for the delivery date to be considered rather robust.

[Insert table 1 about here]

The above are demonstrated in the example of Table 1, where two customers are competing for the same delivery date, however, Customer B withdraws his order since he thinks that the delivery date D2 offered to him is late. On the other hand, Customer A gets an early delivery date D1, however, for some reason such as a more competitive priced vehicle offered by a competing brand in the same time frame, he withdraws too. The method discussed in this paper aims to address the quantification of each of the customer's likelihood to submit or withdraw his order and assist in the process of providing a suitable delivery date that will increase the sale probability. The paper addresses the uncertainty for order submission utilizing the Bayesian networks approach. Production research has adopted a number of models for dealing with the uncertainty in manufacturing.

The majority of the research effort addresses the issues of the information flow and the coordination of manufacturing resources across the production networks. The internet based communication technology can be adjusted to the needs of the customer driven manufacturing [START_REF] Poirier | E-Supply Chain: Using the Internet to Revolutionalize your Business[END_REF][START_REF] Wiendahl | Production in Networks[END_REF], Chryssolouris el al. 2003, Mourtzis el al. 2008, Makris et al. 2008). In a research that evaluated similar problems to the current paper, Tolio and Urgo have discussed the problem of production planning approaches that are considering the availability of complete information and usually fail to deal with real manufacturing environments, characterized by uncertainty affecting the time that the manufacturing operations are executed, the routing of the parts, the requirement of materials and the resources. Their research analysed the issue of negotiation and planning of external resource usage, in a manufacturing system, affected by uncertainty. In particular, the need of resources is considered uncertain and it is modelled through a scenario based formulation [START_REF] Tolio | A Rolling Horizon Approach to Plan Outsourcing in Manufacturingto-Order Environments Affected by Uncertainty[END_REF].

A probabilistic model for decision makers dealing with the uncertainty of equipment selection process (Manassero, Semeraro, Tolio 2004) has been developed and verified in automotive manufacturing, ranking a set of alternative and assigning a probability that the ranking remains stable even in case of uncertainty in assumptions. In addition, a genetic algorithm based dynamic scheduler and a distributed, agent-based shop floor control system have been implemented aiming at systems which can handle critical complexity, reactivity, disturbance and optimality issues at the same time (Monostori, Kádár and Hornyák 2007).

Monostori discussed the process of applying to manufacturing, pattern recognition techniques, expert systems, artificial neural networks, fuzzy systems and hybrid artificial intelligence (AI) techniques. In addition, hybrid AI and multi-strategy machine learning approaches were discussed. Agent-based (holonic) systems were highlighted as promising tools for managing complexity, changes and disturbances in production systems. The additional integration of more traditional AI and ML techniques, with the agent-based approach in the field of intelligent machines, can be predicted resulting in systems with emergent behaviour [START_REF] Monostori | AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing[END_REF].

In the literature, customer behaviour modelling has been discussed for identifying the way that the wealth of data in databases can be used for the evaluation of customers' According to Bounsaythip and Runsala's report, the current methods of estimating a customer's behaviour, are based on building their databases with a significant amount of data, in order for them adapt to the needs of each customer. Methods such as Neural networks, K-Means clustering, Self organising maps, decision trees have been proposed in the literature for performing data mining and data clustering for customer profiling (Bounsaythip and Runsala, 2001). Song et al. have developed a methodology that detects changes of customer behaviour automatically from the customer profiles and the data of sales at different time snapshots [START_REF] Song | Mining the change of customer behavior in an internet shopping mall[END_REF]. Pasek et al., attempted to quantify the way that the combined effects of individual decision making, under abundant choice conditions, impact the model defining optimal variety on the firm's level, in an effort to integrate the information flow between the product design and marketing [START_REF] Pasek | Modeling of Customer Behavior in a Mass-Customized Market[END_REF]). Kwan et. al., developed constructs for measuring the online movement of e-customers, and used a mental cognitive model to identify the four important dimensions of the e-customer behaviour, abstracted their behavioural changes by developing a three-phase e-customer behavioural graph, and tested the instrument via a prototype that used an online analytical mining (OLAM) methodology. A prototype with an empirical Web server log file wasused for verifying the feasibility of the methodology [START_REF] Kwan | An e-customer behavior model with online analytical mining for internet marketing planning[END_REF].

All the methods mentioned above, are actually performing customer segmentation and profiling, based on a wealth of data with reference to the customer. The method proposed in the current paper, needs to evaluate a customer, without having his historical data. The added value of the method is to utilize knowledge of the domain's specialist and to introduce the critical factors that make a model quantify the buyer's likely decision. The specialist's knowledge comprises the specific factors that influence the customer's decision as well as the weight of The factors influencing the customer's behavior are discussed in section 2 and the Bayesian networks' model is developed according to these factors. The use of this method is demonstrated in a typical automotive industrial case study.

Model analysis

Law of total probability and Bayes theorem

This work is based on two basic mathematical principles which are outlined below, the Law of Total Probabilities as well as the Bayes' theorem.

According to the Law of total Probabilities, the probability of an incident A 1 is the sum of the probability of every incident B n , multiplied with the probability of the incident A given the B n [START_REF] Everitt | The Cambridge dictionary of statistics[END_REF][START_REF] Schay | Introduction to probability with statistical applications[END_REF].

P(A 1 ) = [P(A 1 |B 1 )*P(B 1 )] + [P(A 1 |B 2 )*P(B 2 )] +…..+[P(A 1 |B n )*P(B n )]
(1)

Alternatively it can be expressed as:

P(A 1 ) = ∑ [P(A 1 |B n )*P(B n )] (2)
It is possible to combine incidents and represent them graphically, by constructing a belief network, a typical example of which is shown in Figure 1.

The Bayes' theorem relates the conditional and marginal probabilities of stochastic events A and B [START_REF] Everitt | The Cambridge dictionary of statistics[END_REF][START_REF] Schay | Introduction to probability with statistical applications[END_REF].

P(A|B) = [ P(B|A)*P(A)] / P(B) (3)
The terms in the Bayes' theorem have the following conventional meaning: • P(A) is the "prior" probability of A. It is "prior" in the sense that it does not take into account any information about B. • P(A|B) is the conditional probability of A, given B. It is also called the posterior probability because it derives from or depends upon the specified value of B. • P(B|A) is the conditional probability of B given A.

• P(B) is the prior or marginal probability of B, and acts as a normalizing constant. This paper discusses a set of factors for modelling a customer's likely decision about submitting an order for a highly customised product or not. These factors can be used for quantifying the customer's decision. The paper demonstrates the means of possibly utilising the Bayesian networks method in order for these factors to be modelled.

Bayesian network for calculation of probability

A factor identified to be having an impact on the probability of sale, in the context of this work and case study, is the vehicle's driving quality. This relationship is depicted by the Bayesian network in Figure 1. The factor GoodDrive models the quality of drive and has three potential states, Excellent, Moderate and Adequate. Similarly, the probability of sale is modeled by the node ProbabilityofSale and has three potential states: High, Medium and Low.

[Insert Figure 1 [START_REF] Rabiner | A tutorial on hidden Markov models and selected applications in speech recognition[END_REF]).

[Insert Figure 2 about here] This paper has defined nineteen factors that influence a customer's decision to proceed with submitting his order. The factors are discussed later in section "Modelling the customer's likely decision". However, in case that a second factor, such as the CompetitivePrice factor is modeled with the previous approach, then the Bayesian network of Figure 3 is obtained.

[Insert Figure 3 about here]

To evaluate the impact of both factors, the GoodDrive and the CompetitivePrice factors on the ProbabilityofSale, it is necessary that the CPT be filled in as shown in Figure 4. The table size has grown dramatically to be 3x3x3=27, combining all three potential states of the three nodes.

[Insert Figure 4 about here]

The size of the CPT depends on the number of states (s), the number of parents (p), and the number of parent states (s p ) in the following way [START_REF] Gerssen | Bayesian Network Learning for Rare Events[END_REF]:

size(CPT ) = s × (s p ) p (4)
For every possible combination of parent states, there is an entry listed in the CPT.

Therefore, for a large number of parents the CPT will expand drastically. In real life cases, as the one examined in this paper, the model has nineteen parent nodes influencing the node It is obviously impractical to fill in a CPT that requires this number of entries, therefore, an alternative way of approaching it is necessary.

Reverse Bayesian network for calculation of probability of sale

This work suggests the reverse process for conducting Bayesian inference. A simple example is discussed to clarify the use of the Law of total probabilities and the Bayes' theorem in this work.

In the following Bayesian network, the impact of ProbabilityofSale to the GoodDrive node is examined as shown in Figure 5. It is assumed that the probability that a customer will buy a vehicle is known. Therefore, the most likely state to occur from the ProbabilityofSale node is known. In addition, it is known that high probability of sale is most likely when the drive quality of the vehicle is also high. This is modeled by the conditional probabilities table that relates the ProbabilityofSale with GoodDrive potential states and is shown in Figure 6.

Then, with the use of the Law of total probability, it is possible for the probabilities of the GoodDrive states to be calculated.

[Insert Figure 5 about here] [Insert Figure 6 about here] P(Excellent) = P(Excellent|High)*P(High) + P(Excellent|Medium)*P(Medium)]+ P(Excellent|Adequate)*P(Adequate)

Substituting the probabilities, we obtain: P(Excellent) = 0,95791*1 + 0,04039*0,00 + 10 -20 *0,00 = 0,95791 that is 95,791 % 96%.

Therefore, given that the probability of sale is high, the customer would prefer a car that offers Excellent drive by 96%. This is a result that follows also common sense. Based on the above, it is possible to reverse the inference logic by considering the Bayes theorem and utilizing the prior probability that a customer will be having particular preferences about the vehicle's drive quality. This is represented by the GoodDrive node, and then the likelihood that a state of the ProbabilityofSale node will occur will be calculated.

[Insert Figure 7 about here] This is demonstrated by the example in Figure 7, by applying the Bayes Theorem, it is possible for the node ProbabilityofSale from the node GoodDrive to be derived. The benefit of this reverse process is that we can calculate the ProbabilityofSale node by filling in a 3x3 CPT for the link of the ProbabilityofSale with the node GoodDrive. For every additional link that is added to the Bayesian network, following this reverse approach, a single CPT 3x3 in size has to be filled in; it is therefore a manageable task. This approach will be generalized in the following section and will be demonstrated in the form of a realistic model.

Industrial case study

This section demonstrates the application of the reverse decision making method in an automotive industry case. Initially, the most important factors that influence a customer's decision are outlined and afterwards they are modeled with the help of the Bayesian networks method.

Modelling the customer's likely decision

The major factors that influence a customer's decision to place a vehicle order have been identified and analyzed after industry experts have been interviewed. These factors are presented shortly as follows:

[Insert table 2 about here] These factors have been modelled as nodes in a Bayesian network. Each factor has three potential states that evaluate the customer's perception of the factor. For example, the factor NeedTheVehicleEarly, has three potential states:

• No requirements, if the customer has no special requirements about the time he receives the vehicle, • Insignificant requirements, if he has some preference but of minor urgency, • Specific requirements, if he has strict time requirements, e.g. he needs the vehicle as soon as possible for a family trip and his old car has broken down.

All the factors have been modeled in the same way and in three potential states. This model is represented in the following table.

[Insert table 3 about here]

The Bayesian network that links each of these factors with the customer's likely decision is shown in Figure 8. In terms of the Bayesian networks, linking two nodes, e.g. the node GoodDrive with the node CustomersDecision, requires that the CPT be filled in. The CPT of the specific relation is seen in Figure 9.

Conditional probabilities tables' modeling

Filling in the CPTs can be done by the following options:

• An expert fills in the table based on his experience.

• Use past data to teach the network to adopt one of the methods found in the literature • Use equations to link the two nodes and generate the CPT data.

In this particular paper a method that utilizes the expert knowledge and the use of equations has been adopted. In particular, a normal distribution has been used to generate the CPT data for each condition of node CustomersDecision the value node GoodDrive would have a likelihood around a mean value with a standard deviation. In each state of the node GoodDrive a value has been allocated as follows:

• State Excellent has the value of 10,

• State Moderate has the value of 5,

• State Adequate has the value of 0.

In this case, the probability of sale is affected by the quality of drive as follows:

• The probability of sale would be more likely if the drive quality was Excellent,

• the probability of sale would be less likely if the drive quality was Moderate,

• the probability of sale would be very low if the drive quality was Adequate.

The above have been modeled by having adopted a Normal Distribution linking that of the CustomersDecision with the factor DriveQuality, as follows. If the node CustomersDecision is a Yes then, the node GoodDrive will have a value of 10 with a standard deviation of 2;

actually, this means that achieving a Yes decision is more likely provided that the GoodDrive CustomersDecision case where both state Yes and No have the same probability linked to a value of 5 of the factor GoodDrive with a standard deviation of 2; a Low probability of sale is linked to a value of 0 of the factor GoodDrive with a standard deviation of 1.

The CPTs that are generated from the above mentioned distributions for the specific link, between the CustomersDecision and GoodDrive, can be seen in Figure 9. Reading this table, in 59,9 % of the cases that a CustomersDecision was Yes, the GoodDrive value node would be Excellent.

From the above, it occurs that a 2 x 3 CPT should be built for linking each

CustomersDecision node with each individual node. Therefore, it is necessary that nineteen CPTs, 2x3 in size be filled which is rather manageable compared with the case that a CPT, 3.486.784.401 in size should be filled in, as shown in section " Bayesian network for calculation of probability".

[Insert Figure 9 about here]

Web application software implementation

The Bayesian network model has been built with the use of the Netica® commercial software package [START_REF] Norsys | Netica software[END_REF]. Netica was chosen because it offered a Java API rich enough to allow the programming of the Bayesian network in the form of a web application.

A web application was developed in order for end users to be assisted at manipulating the model in a user friendly way. A screenshot of the Bayesian inference web application can be seen in Figure 10. The end user being typically a dealer, at the dealership, is entering his assessment about the customer, based on the factors that were defined in Table 2. The application follows the 3-Tier paradigm and consists of the following layers as seen in Figure 11:

• the Graphical user interface layer that is built with the use of the Java Server Pages-JSP (SUN-JSP 2009), • the business logic layer that is written in Java and is accessed by the user via the JSP pages,

• the data repository in this application, is a set of plain text files that stores the Bayesian inference data, the CPTs, the nodes, the states and the result of the Bayesian inference [Insert Figure 11 about here]

The outcome of the inference can be seen in Figure 12. According to this evaluation, the customer will most likely place his order, 56,7%, however, there is another 43,3% that he will not be placing his order; the assessment in this case, is rather positive. The result of the evaluation is a relative measure and can be used for comparing alternative assessments that are performed for a customer. This means that the model evaluates a value per node in the Bayesian network. Each value of each node, corresponds to a potential scenario; for example, a potential value "Fully" in the node "ComplyToTimeRequirements" reflects a scenario that a vehicle is promised to be delivered to the customer early enough to address his time requirements. In this case, it is likely that the state Yes of the CustomersDecision node would be let's say 90%, In a similar way, a different delivery date could be promised to the customer being later than his requested delivery date. In this case, the node "ComplyToTimeRequirements" would be set to "NoComply". In this case, the state Yes of the CustomersDecision node would probably be lower than in the previous case, let's say 50%. Therefore, the states of the node CustomersDecision can be used as a measure for comparing different sets of values in each node of the Bayesian network. In case that a customer receives an evaluation of 50% for a set of 15 factors and for another, the same customer receives a 90%, then the 90% is better and more preferable since it increases the probability for the customer to buy the product. Therefore, the model actually quantifies the impact of the change of delivery date.

[Insert Figure 12 about here]

Discussion

Currently, there is a high uncertainty whether a customer will place an order for a vehicle under a certain delivery date and cost conditions. The method that was previously analyzed can be used for quantifying the likelihood that customers will place an order. This likelihood can then be used in order for the customer to be offered a vehicle earlier, in case that his time requirements

were not addressed and his likely behavior would be not to place the order. In this case though, the supply chain will need to react much faster in order to supply the necessary material necessary for the customer's order, by imposing a cost of customization [START_REF] Mourtzis | Supply chain modeling and control for producing highly customized products[END_REF]).

However, this cost is balanced by the fact that the vehicle is produced for a final customer and not for a dealer thus, it is sold at the best possible price. On the other hand, if the probability for a customer to place an order is rather high, then a vehicle that is produced a bit later, based on the normal scheduling procedure, will be chosen and no additional customization activities are necessary.

Conclusions

This paper described a method of evaluating a customer's likely response, under a specific delivery time and requesting a highly customised product. The paper discussed the impact of such a customer's behavioural evaluation on the manufacturing planning and the importance of having a method for quantifying the customer's likely decision as to whether to submit an order or not. The method demonstrated the integration of the Bayesian networks theory into a real life industrial case. These methods are usually quite theoretical and difficult to be used, due to the mathematical background required. The paper has demonstrated a reverse decision method of simplifying the decision making process. A method was analysed to avoid entering the large amount of conditional probabilities that the typical Bayesian network would require. The paper suggested that the structure of the Bayesian network be reversed, thus minimise the necessary amount of data required for performing the Bayesian inference. The integration of the method into a web based software tool is simple enough and enables end users to use it in real life situations.

In addition, the paper demonstrated a method for capturing the knowledge of the domain's specialist and introduced the critical factors that make a model quantify the buyer's likely decision. The method is able to evaluate a customer, having a limited amount of data about him. The specialist's knowledge comprises the specific factors that influence the customer's decision as well as the weight of each factor on the total decision. The Bayesian network proposed, models these factors and assigns conditional probabilities to model the importance of each factor. The paper demonstrated that the Bayesian networks method wassuitable for quantifying a customer's likely decision and could be used effectively formodelling a set of factors that determine his behaviour.

Further work is necessary for the tool to be integrated as a module of a supply chain control tool for automatically assessing a customer's likely response, under the different delivery dates of the vehicle.

F

  the total decision. The Bayesian network proposed models these factors and assigns conditional probabilities for modelling the importance of each factor.

9

  ProbabilityofSale the size of the CPT would be: size(CPT ) = s × (s p ) p = 3 x 319 = 3.486.784.401.

  is feasible to reverse the probabilities calculation method by the adoption of the Bayes theorem. This reversing approach is very important since it can be utilized to quantify the ProbabilityofSale, based on a number of other factors by first structuring a Bayesian network of factors having been influenced by the ProbabilityofSale and then by reversing the calculation as shown above, the ProbabilityofSale is finally calculated.

  Excellent since Excellent is mapped to a value of 10. In a similar way, a

Figure 7 :FigureFigure 4 .Figure 5 .Figure 6 .Figure 9 .

 74569 Figure 7: Impact of the node GoodDrive on node ProbabilityofSale

  about here] According to the Bayesian networks' principles, to calculate the state of the ProbabilityofSale, with the highest probability to occur, it is necessary that the Conditional Probabilities Table, seen in Figure2, to be defined. This table defines the probability of the state High occuring as soon as the state Excellent, Moderate, Adequate occur in the node GoodDrive.The table has a size of 3x3, that is three factors from the node GoodDrive and three from the Filling in this table is done either manually by experts or by utilizing historical data

	node ProbabilityofSale.
	F o
	r
	P
	e e r
	R e v i e w
	O n
	l y
	8

Table 1 .

 1 Competing orders from individual customers during the order promising process

	from Ford of Europe and Mrs Francesca Di Luccio from Centro Richerce Fiat for supporting this
	research effort.
	o
	r
	P
	e e r
	R e v i e w
	O n
	l y
	17

URL: http://mc.manuscriptcentral.com/tandf/tcim Email:ijcim@bath.ac.uk

URL: http://mc.manuscriptcentral.com/tandf/tcim Email:ijcim@bath.ac.ukInternational Journal of Computer Integrated Manufacturing

Acknowledgments

This work has been partially supported by the research project "MyCar", funded by the CEU.

The authors would like to express their gratitude to Mr Peter Cowburn, Mr Domingo Lapadula


Zbyšek Pavlík

Robert Erný

Hygrothermal Performance Study of an Innovative Interior Thermal Insulation System

Keywords: interior thermal insulation system, hydrophilic mineral wool, water vapor retarder, hygrothermal performance

An innovative interior thermal insulation system on the basis of hydrophilic mineral wool which can serve as an alternative solution to the commonly used systems with water vapor barrier is presented in the paper. At first, the process of materials design is described. Then, the hygrothermal performance of the designed insulation system is tested in the difference climate conditions that correspond to the winter climate in Middle Europe. In the experiment, the profiles of temperature, relative humidity and liquid moisture content are monitored. Measured temperature profiles demonstrate the proper thermal insulation function of the system. The hygric function can also be considered very good as no water condensation during the whole testing period of five months appears in the insulation layer. Therefore, the basic requirements for the successful application of the system in building practice are met.

Introduction

Energy crisis in 1973 has claimed attention of building physicists, designers and building materials producers. Since that time, it was not able to construct buildings fulfilling mainly static function and forming interior space. For energy savings reasons, the indoor quality, thermal performance of buildings and hygienic requirements became suddenly focus of designers and investors. The hygienic quality of indoor space was until this time provided by natural infiltration of exterior air through building envelopes structures (above all trough the windows joints) and the thermal losses were fully compensated by heating systems [START_REF] Hens | Building Physics -Heat, Air and Moisture, Fundamentals and Engineering Methods with Examples and Exercises[END_REF]. However, this state was not sustainable. Because of rapid increase of heating expenses, it was necessary to solve the problem of thermal insulation of newly developed as well as of existing buildings.

The contemporary progress in material base of building materials is characteristic by rapid development of new thermal insulation materials and design of new structural and technological solutions. The progress is aimed at lower thermal losses of buildings, and answers the strict requirements of thermal technical standards. Various thermal insulation systems taking advantages of different types of thermal insulation materials on both organic (expanded plastics, wood wool, cork, straw, technical hemp) and inorganic basis (foamed glass, glass and mineral fibers) are being designed and tested, and new methods for analyzing the properties of both insulation materials and insulation systems are being devised [START_REF] Khedari | New low-cost insulation particleboards from mixture of durian peel and coconut coir[END_REF]- [START_REF] Wei | Effects of structure and shape on thermal performance of perforated multilayer insulation blankets[END_REF]. The particular products differ in their shape, flammability, composition and structure, what in a relation to designers' requirements assigns the possibilities of their application in building practice.

Exterior and interior thermal insulation systems

For thermal insulation of existing and newly built structures, exterior thermal insulation systems are the most frequently applied. They are capable of formation of compact insulation layers; herewith the possible thermal bridges that can lead in specific cases to significant increase of thermal losses are easily eliminated. Application of exterior thermal insulation systems also reduces the thermal loading of envelope structures (especially of their joints) that are usually exposed to the negative effects of temperature and weather changes. The exterior insulation can be realized in the form of a contact system or with air gap. The contact insulation systems form compact structure. The thermal insulation plays here also the bearing role for surface layers. The facade surfaces are made mostly of plasters and sometimes of glued facings. In the case of systems with air gap the thermal insulation layer is fixed between the parts of bearing grid that supports the facade elements. These can be formed by glass, metal, wood, cement composites, ceramic materials, etc.

On the other hand, the interior thermal insulation systems can be found in building practice relatively rarely. The main reason for the less common usage of interior systems is that their application brings many problems from the view of building physics that must be taken into account and solved in advance. Nevertheless, sometimes there is no other reasonable chance than the application of the interior thermal insulation systems. A typical example is historical buildings where the preservation of original architectonical view of facades is one of the top interests of conservationists. The interior systems can also find use in buildings having complex facade surfaces.

The most obvious problems of interior insulation systems present thermal bridges that can not be fully eliminated. Another problem is the temperature differences in envelope walls that are not protected against external environment, thus the masonry can be faster deteriorated. The low heat accumulation in envelope structures and reduction of indoor space notably limit the possibilities of interior insulation systems as well.

Probably the most serious problem typical for interior thermal insulation systems is the risk of water vapor condensation in the thermal insulation layer. It can be prevented in two basic ways. A standard solution to this problem consists in application of vapor barrier (e.g. on polyethylene foil principle) under the internal plaster on the surface of the insulation layer, so that both the insulation layer and the load bearing structure are protected against water vapor. The risk of this construction solution consists in fact that the foil vapor tight barrier can be mechanically damaged. Afterwards, the vapor condensation can occur and the thermal insulation function will be reduced. In addition, even in the case that the barrier would perform without mechanical damage, the absence of water vapor removal from the interior through the envelope in the winter period, when the air ventilation in the interior is usually limited, can lead to an undesirable increase of relative humidity in the interior and to the worsening of the internal microclimate.

The second possible solution of the risk of water vapor condensation in the interior thermal insulation is utilization of capillary active or hydrophilic thermal insulation materials. The first steps in that direction were done during the last couple of years [START_REF] Pavlík | Interior Thermal Insulation System Based on Hydrophilic Mineral Wool[END_REF][START_REF] Pavlík | Experimental Assessment of Hygrothermal Performance of an Interior Thermal Insulation System Using a Laboratory Technique Simulating On-Site Conditions[END_REF]. The basic idea of the interior thermal insulation system designed in [START_REF] Pavlík | Interior Thermal Insulation System Based on Hydrophilic Mineral Wool[END_REF] consisted in the change of usually employed composition of the building envelope formed from the exterior to the interior by load bearing structure, thermal insulation, vapor tight layer and interior plaster by the following: load bearing structure, water vapor retarder having exactly specified water vapor diffusion resistance factor (allowing only such amount of water vapor to transfer which could not condensate in the load bearing structure), hydrophilic thermal insulation (special type of mineral wool) and water vapor permeable plaster. The system was proved to be effective in several specific cases.

However, a universal solution valid for arbitrary load bearing structure was not found yet.

In this paper, the work done in [START_REF] Pavlík | Interior Thermal Insulation System Based on Hydrophilic Mineral Wool[END_REF], [START_REF] Pavlík | Experimental Assessment of Hygrothermal Performance of an Interior Thermal Insulation System Using a Laboratory Technique Simulating On-Site Conditions[END_REF] is continued with new materials and new ideas for the solution of water vapor transport and water condensation problems typical for interior thermal insulation systems. The methods of directed design are applied to the materials of the chosen interior thermal insulation system; they consist of a combination of computational simulation techniques, laboratory experimental work aimed at the hygric and thermal properties of the studied materials and semi-scale experimental verification of the functionality of the system in the conditions of difference climate.

The materials of the thermal insulation system resulting from this type of design should be applicable universally, without restricting requirements and presumptions as for the material of load bearing structure.

Materials design of the innovative interior thermal insulation system

In the process of materials design, a detailed computational analysis of the studied problem was performed at first. A special attention was paid to the hygric and thermal transport and storage properties of hydrophilic thermal insulation material and water vapour retarder. Contrary to the previous investigations which were done in [START_REF] Pavlík | Interior Thermal Insulation System Based on Hydrophilic Mineral Wool[END_REF], [START_REF] Pavlík | Experimental Assessment of Hygrothermal Performance of an Interior Thermal Insulation System Using a Laboratory Technique Simulating On-Site Conditions[END_REF],

an additional condition was imposed that the resulting interior thermal insulation system should be independent of the material of the load bearing structure. The computational simulations were performed using the TRANSMAT computer simulation tool [START_REF] Mad Ra | TRANSMAT -A Computer Simulation Tool for Modeling Coupled Heat and Moisture Transport in Building Materials[END_REF] where the model of coupled heat and moisture transport in multi-layered building envelope systems formulated by Künzel [START_REF] Künzel | Simultaneous Heat and Moisture Transport in Building Components[END_REF] was used. The parameter data for the materials of load bearing structure (ceramic brick, sandstone, argillite, granite, concrete) were taken from the TRANSMAT material database. The data for hydrophilic thermal insulation material and water vapour retarder were the free parameters in the computations.

The data of heat and moisture transport and storage properties of hydrophilic thermal insulation material and water vapour retarder which in computational simulations led to the proper hygrothermal performance of the system were passed to the material producers and were taken into account in the materials' manufacturing. The producers then delivered the following materials which were supposed to conform to the requirements given by the computational analysis: hydrophilic mineral wool Dachrock (Rockwool, Inc.), water vapor retarder SM-T FLT -a material on flexible cement glue basis (Mamut-Therm, Ltd.), and interior plaster SM-TJ VJ (Mamut-Therm, Ltd.) on lime basis.

In the further step of the materials design procedure, the measurement of basic parameters of manufactured materials was done. The reason for doing these experiments was to verify whether the requirements on material properties identified in the computational design were really met. The measured material parameters which are shown in Table 1 (ρ is the bulk density, c specific heat capacity, κ moisture diffusivity, µ water vapor diffusion resistance factor, λ dry thermal conductivity in dry state, λ sat thermal conductivity in water saturated state, θ sat saturation moisture content and θ hyg hygroscopic moisture content) were then used as input data for the final computational testing of the hygrothermal performance of the system.

A comparison of the properties of the new hydrophilic mineral wool Dachrock with the other types of mineral wool which were analyzed before in [START_REF] Ji I Ková | Effect of hydrophilic admixtures on moisture and heat transport and storage parameters of mineral wool[END_REF] showed that the moisture diffusivity of Dachrock was four to five orders of magnitude higher than moisture diffusivity of common mineral wools and even one to two orders of magnitude higher than for the previous types of hydrophilic mineral wool. This was the key factor for the successful use of Dachrock in the innovated interior thermal insulation system.

The extremely high value of moisture diffusivity was achieved by a combination of two modes of liquid water transport in the material. The first was the surface transport of water along the fibers provided by hydrophilic surface treatment. The second was capillary water transport in the pores which were smaller than in previous types of hydrophilic mineral wool due to the higher density of Dachrock; this led to a substantial magnification of capillary effects.

The computational simulations with the real data for thermal and hygric parameters of the materials of the interior thermal insulation system confirmed the proper hygrothermal function of the system in several modifications as for the load bearing structure [START_REF] Mad Ra | Computational Design of Hygric and Thermal Properties of Materials in Multi-Layered Building Envelope Systems: A Good Prevention Of Damage[END_REF]. It was shown that the condensate annual balance of the structure was negative in the first year. The load-bearing structure was dried-out during the first summer after the application of the interior thermal insulation system. In the subsequent four years, no overhygroscopic moisture appeared in the whole structure. Based on the promising results of the computational simulations, an experimental hygrothermal performance study of the whole system in the conditions close to reality on building site could be done as the final step of the design process.

Experimental setup for the hygrothermal performance study

The hygrothermal performance study was performed for the interior thermal insulation system described in the previous section which was applied on the 300 mm thick brick wall (Fig. 1). The tested structure was exposed to the difference climate conditions corresponding to the typical winter period, which is the most critical part of the year from the point of view of water condensation in building structures. On the exterior side, climatic hourly data for the time interval of November 1 to March 27 were simulated according to the test reference year (TRY) data for Prague obtained by meteorological measurements. On the interior side, the wall was exposed to natural climatic conditions in the testing laboratory which corresponded to the values typically observed in residential houses in Czech Republic (relative humidity 20-60%, temperature 20-26°C).

The experiment was performed using the measuring and simulation system NONSTAT (Fig. 2, see [START_REF] Pavlík | System for Testing the Hygrothermal Performance of Multi-layered Building Envelopes[END_REF] for details). The system consisted of a climatic chamber for simulation of external climatic conditions which was connected to an external tunnel provided with thermally insulated walls. The analyzed specimen was placed inside the tunnel. In order to ensure 1-D heat and moisture transport conditions, the specimen was water-and vaporproof insulated on all lateral sides and the space between the specimen and the walls of the tunnel was filled with foamed polyurethane. The external face of the tunnel with the specimen was exposed to the laboratory environment. In the tested structure, the monitoring of liquid moisture content, relative humidity and temperature was performed.

The liquid moisture content was measured using a time-domain reflectometry (TDR) device by Easy Test, Ltd. TDR is a microwave moisture measurement method based on relative permittivity assessment. The particular device used in the experiment works in the frequency range of 50 MHz to 2 GHz [START_REF] Ji I Ková | Application of TDR Microprobes, Minitensiometry and Minihygrometry to the Determination of Moisture Transport and Moisture Storage Parameters of Building Materials[END_REF]. The accuracy of moisture content measurement given by the producer is ± 2% in the water saturation range of 0-100%. For monitoring relative humidity and temperature, commercially produced combined sensors by Ahlborn were employed. The accuracy was as follows: capacitive humidity sensors were applicable in the humidity range of 5-98% with the accuracy of ± 2%, resistance thermometers had the accuracy of ± 0.4°C in the temperature range of -20°C to 0°C, and ± 0.1°C in the temperature range of 0°C to 70°C. The positioning of the particular sensors is shown in Fig. 3. The relative humidity profiles are given in Fig. 5. In the brick masonry, rather high relative humidity values were observed which corresponded with the measured liquid moisture profiles (Fig. 6). The content of liquid moisture was typically between 3 and 5%. The presence of this kind of moisture in the wall is a result of wetting of the studied structure during the specimen preparation. It did not evaporate until the end of the experiment because of applied winter climatic conditions which were chosen on purpose as the worst-case scenario. Nevertheless, we have good reason for conclusion that the liquid water is supposed to evaporate during the next summer period as it was proved in computational simulations.

Experimental results and discussion

In the whole thickness of the thermal insulation board, the relative humidity values were lower than maximum hygroscopic moisture content and the liquid moisture content was equal to zero. So, no water vapor condensation in the insulation layer or on its surface appeared during the whole winter period. This was an apparent consequence of the very high values of water transport parameters of the hydrophilic mineral wool used in the insulation system.

Conclusions

The hygrothermal performance study presented in this paper can be considered as further step towards routine application of interior thermal insulation systems without water vapour barrier in building practice. Contrary to the previous work which was done with this type of insulation systems in the past ( [START_REF] Pavlík | Interior Thermal Insulation System Based on Hydrophilic Mineral Wool[END_REF], [START_REF] Pavlík | Experimental Assessment of Hygrothermal Performance of an Interior Thermal Insulation System Using a Laboratory Technique Simulating On-Site Conditions[END_REF]), the proposed innovative solution does not depend on the type of load bearing structure. This was achieved mainly due to the remarkable improvement of water transport properties of the advanced types of hydrophilic mineral wool which lowered the significance of the properties of water vapour retarder for the proper function of the insulation system. Table 1 Basic parameters of the materials designed for the innovated interior thermal

Fig. 4

 4 Fig. 4 presents temperature profiles in the insulated brick wall which clearly demonstrate

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Figure 2 combined

 12345612 Figure captionsFigure 1 Scheme of the studied structure

Acknowledgements

This research has been supported by the Ministry of Education, Youth and Sports of Czech Republic, under project No MSM: 6840770031.