Tanja Schultz

Michael Wand

Modeling Coarticulation in EMG-based Continuous Speech Recognition

Keywords: EMG-based Speech Recognition, Silent Speech Interfaces, Phonetic Features

This paper discusses the use of surface electromyography for automatic speech recognition. Electromyographic signals captured at the facial muscles record the activity of the human articulatory apparatus and thus allow to trace back a speech signal even if it is spoken silently. Since speech is captured before it gets airborne, the resulting signal is not masked by ambient noise.

The resulting Silent Speech Interface has the potential to overcome major limitations of conventional speech-driven interfaces: it is not prone to any environmental noise, allows to silently transmit confidential information, and does not disturb bystanders.

We describe our new approach of phonetic feature bundling for modeling coarticulation in EMG-based speech recognition and report results on the EMG-PIT corpus, a multiple speaker large vocabulary database of silent and audible EMG speech recordings, which we recently collected. Our results on speaker-dependent and speaker-independent setups show that modeling the interdependence of phonetic features reduces the word error rate of the baseline system by over 33% relative. Our final system achieves 10% word error rate for the best-recognized speaker on a 101-word vocabulary task, bringing EMG-based speech recognition within a useful range for the application of

Introduction

In the past decade, the performance of automatic speech processing systems, including speech recognition, spoken language translation, and speech synthesis, has improved dramatically. This has resulted in an increasingly widespread use of speech and language technologies in a large variety of applications, such as commercial information retrieval systems, call center services, voice-operated cell phones, car navigation systems, personal dictation and translation assistance, as well as applications in military and security domains. However, speech-driven interfaces based on conventional acoustic speech signals still suffer from several limitations. Firstly, acoustic speech signals are transmitted through air and are thus prone to ambient noise. Despite tremendous efforts there are still no robust speech processing systems in sight, which provide reasonably good results in crowded restaurants, airports, or any noisy places. To overcome this problem we propose to capture and process the speech signal before it gets airborne and thus avoid to get affected by adverse noise conditions.

Secondly, conventional speech interfaces rely on audibly uttered speech, which has two major drawbacks: it jeopardizes confidential communication in public and it disturbs any bystanders. Services which require the access, retrieval, and transmission of private or confidential information, such as PINs, passwords, and security or safety information are particularly vul-nerable. The proposed Silent Speech Interface (SSI) allows to utter speech silently and thus overcomes both limitations: confidential information can be submitted securely and silent speech does not disturb or interfere with the surroundings.

Finally, Silent Speech Interfaces might give hope to people with certain speech disabilities as the technologies allow the building of virtual prostheses for patients without vocal folds [START_REF] Denby | Silent Speech Interfaces[END_REF]. Also, elderly and weak people may benefit since silent articulation can be produced with less effort than audible speech.

Our approach to capture speech before it gets airborne relies on surface ElectroMyoGraphy (EMG). This is the process of recording electrical muscle activity using surface electrodes. When a muscle fiber is activated by the central nervous system, small electrical currents in form of ion flows are generated. These electrical currents move through the body tissue, encountering a resistance which creates an electrical field. The resulting potential differences can be measured between certain regions on the body surface, i.e. at the skin. The amplified electrical signal obtained from measuring these voltages over time can be fed directly into electronic devices for further processing. Since speech is produced by the activity of the human articulatory muscles, the resulting myoelectric signal patterns allow to trace back the corresponding speech. These signals are not corrupted or masked by environmental noise transmitted through air. Furthermore, since EMG relies on muscle activity only, speech can even be recognized when it is produced silently, i.e. mouthed without any vocal effort.

We envision several application areas for Silent Speech Interfaces: (1) robust, private, non-distracting speech recognition for human-machine interfaces, such as silently speaking text messages, (2) recognition plus speech synthesis (at the remote side) for quietly accessing remote applications, such as speech or text-based information systems, [START_REF] Kopf | [END_REF] transmitting articulation parameters followed by articulatory synthesis for silent human-human communication, (4) speech prostheses, and (5) recognition of silent speech followed by text translation into another language followed by speech synthesis, which appears like speaking in a foreign tongue. In 2006 we successfully demonstrated a prototype of this last application at Interspeech [START_REF] Jou | Towards Continuous Speech Recognition using Surface Electromyography[END_REF].

A video file of our latest system showcasing some of the above mentioned applications is available from our webpage1 .

Toward Large Vocabulary EMG-based Speech Recognition

The use of EMG for speech recognition dates back to the mid 1980s, when Sugie and Tsunoda in Japan, and Morse with colleagues in the United States published almost simultaneously their first studies. [START_REF] Sugie | A Speech Prosthesis Employing a Speech Synthesizer -Vowel Discrimination from Perioral Muscle Activities and Vowel Production[END_REF] used three surface electrodes to discriminate Japanese vowels, and demonstrated a pilot system which performed this task in realtime. [START_REF] Morse | Research Summary of a Scheme to Ascertain the Availability of Speech Information in The Myoelectric Signals of Neck and Head Muscles using Surface Electrodes[END_REF] examined speech information from neck and head muscle activity to discriminate two spoken words, and in the following years, extended their approach to the recognition of ten words spoken in isolation [START_REF] Morse | Use of Myoelectric Signals to Recognize Speech[END_REF][START_REF] Morse | Speech Recognition Using Myoelectric Signals with Neural Networks[END_REF]. Although initial results were promising, with accuracy rates of 70% on a ten word vocabulary, performance decreased dra-matically for slightly larger vocabularies, achieving only 35% for 17 words, and thus did not compare favorably with conventional speech recognition standards.

More competitive performance was first reported by [START_REF] Chan | Myoelectric Signals to Augment Speech Recognition[END_REF], who achieved an average word accuracy of 93% on a 10-word vocabulary of the English digits. A good performance could be achieved even when words were spoken non-audibly, i.e. when no acoustic signal was produced [START_REF] Jorgensen | Sub Auditory Speech Recognition Based on EMG/EPG Signals[END_REF], suggesting this technology could be used to communicate silently. Recent work [START_REF] Jou | Towards Continuous Speech Recognition using Surface Electromyography[END_REF][START_REF] Walliczek | Sub-Word Unit Based Non-Audible Speech Recognition Using Surface Electromyography[END_REF] successfully demonstrated that phonemes can be used as modeling units for EMG-based speech recognition by carefully designing the signal preprocessing front-end, paving the way for large vocabulary speech recognition. For a more detailed review on Silent Speech Interfaces based on EMG, please refer to [START_REF] Denby | Silent Speech Interfaces[END_REF] in this journal issue.

While a lot of progress was made over the last years, there are still major limitations which need to be overcome for the application of EMG to large vocabulary speech recognition. In particular, we see three major challenges.

First, the impact of speaker dependencies, such as speaking style, speaking rate, and pronunciation idiosyncrasies needs to be investigated. Second, the EMG signal is affected by changes in electrode positioning, environmental conditions (temperature and humidity), and tissue properties [START_REF] Leveau | Output forms: Data analysis and applications[END_REF]. These factors clearly favor the development of speaker dependent and often session dependent systems, i.e. systems in which training and testing is performed on data collected within the same recording session. Consequently, results known from the literature focus on a very small number of subjects. Third, little is known yet about the qualitative and quantitative articulation differences between silent and audible speech.

Our experimental results in [START_REF] Maier-Hein | Session Independent Non-Audible Speech Recognition Using Surface Electromyography[END_REF] and more recently in (Wand et al., 2009) suggest that EMG signals do significantly differ between silent and audible speaking mode. We assume that this is mainly due to the lack of biofeedback when speaking silently but further investigation will be necessary to continuously improve our silent speech interface. Nevertheless, we recently demonstrated the first speech recognition system that handles seamless switches between both speaking modes.

In this article we focus on the issue of achieving reliable and robust models for large vocabulary speech recognition systems based on EMG and show that these models significantly improve the recognition performance, even when impacting factors such as speaker and session variabilities are present.

The "EMG-Pittsburgh (EMG-PIT)" Multiple Speaker Database

Over the last two years we collected a large database of EMG signals from 78 speakers, where the speakers produced audible and silent, i.e. mouthed, speech. This collection was done in a joint effort with colleagues from the Department of Communication Science and Disorders at University of Pittsburgh [START_REF] Dietrich | The Effects of Stress Reactivity on Extralaryngeal Muscle Tension in Vocally Normal Participants as a Function of Personality[END_REF].

The collection was carried out in two phases, a pilot study with 14 speakers, and the final collection of 64 speakers. The 14 pilot study subjects participated in two recording sessions, the other speakers participated in one recording session. All participants were female adults between 18 and 35 years of age with normal vocal qualities. The subjects were recruited primarily from the student population of Pittsburgh (University of Pittsburgh and Carnegie Mellon University). To study similarities and differences of audible and silent speaking mode, the database covers both speaking modes with parallel utterances. The audible utterances were simultaneously recorded with a conventional airtransmission microphone. For EMG recording we used a computer-controlled 8-channel EMG data acquisition system (Varioport, Becker-Meditec, Germany). Technical specifications of the Varioport system include an amplification factor of 1170, 16 bits A/D conversion, a step size (resolution) of 0.033 microvolts per bit, and a frequency range of 0.9-295 Hz. All EMG signals were sampled at 600 Hz. Following our previous studies on the optimal positioning and number of recording electrodes [START_REF] Maier-Hein | Session Independent Non-Audible Speech Recognition Using Surface Electromyography[END_REF], we adopted the electrode positioning which yielded maximal recognition results. This also ensures backward compatibility of our experiments. The electrode setting is shown in figure 2.1. It uses five channels, numbered 1, 2, 3, 4, and 6. Channel five serves for experiments with different electrode positionings, however we did not use it for the experiments described in this paper. Channels 1, 2, and 6 use bipolar derivation, whereas channels 3, 4, and 5 were derived unipolarly, with two reference electrodes placed on the mastoid portion of the temporal bone. The electrodes capture signals from the levator angulis oris (channels 2 and 3), the zygomaticus major (channels 2 and 3), the platysma (channel 4), the anterior belly of the digastric (channel 1) and the tongue (channels 1 and 6). However, due to the fact that the EMG is captured at the surface, some signals may consist of a superposition of active muscle fibers in the proximity of the recording electrode. The acoustic data were recorded at 16kHz, 16bit resolution and stored in PCM encoding.

All subjects were recorded with a close-up video Camcorder while producing audible and silent speech.

In order to get good phone coverage, and to avoid transcription work, the subjects read phonetically balanced sentences in a controlled setting rather than recording conversational, unplanned speech. To cover large amounts of linguistic context but at the same time allow for mode and variability comparisons, the speaker read one batch of 10 BASE utterances, which are the same for each speaker, and one batch of 40 speaker specific SPEC utterances, only read by one speaker. The vocabulary of the BASE sentences consisted of 101 words. All sentences from both batches were selected to be phonetically balanced. Each recording session consisted of two parts, one audible and one silent speech part. In each part we recorded one BASE set and one SPEC set. The total of 50 utterances were recorded in random order. For

Utterances

Duration the pilot study subjects who recorded two sessions, the order of the audible and silent parts was reversed after the first session to control effects from utterance repetitions between the parts. Table 1 shows the statistics from the resulting EMG-PIT corpus.

Baseline EMG-based Speech Recognition System

In (Wand and Schultz, 2009b) we reported first EMG recognition results based on 26 recording sessions with 13 speakers of the audible part of the EMG-PIT pilot study subset. For each speaker, the audible part of the SPEC set was used for training, and the BASE set for testing. This EMG-based recognizer is described below and serves as a baseline for the experiments presented in this paper.

The baseline EMG-based recognition system uses 45 context independent phoneme models and a silence model. Each phoneme is modeled using a 3-state left-to-right Hidden Markov Model (beginning, middle, and end of the phoneme), silence is modeled by a single state. This results in 136 models, each of which applies Gaussian Mixtures as emission probabilities.

This modeling scheme follows the traditional setup for context-independent acoustic-based speech recognition.

The amount of Gaussians is determined by a merge-and-split algorithm [START_REF] Ueda | Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates[END_REF] on the training data, resulting in roughly 2 Gaussians per model on average. In total the baseline system consists of 290 Gaussians. This small number is due to the very limited amount of training data.

For the same reason our systems applies Gaussians with diagonal covariance matrices.

For feature extraction, we found that time-domain features gave optimal results. This feature extraction method is defined in the following way [START_REF] Jou | Towards Continuous Speech Recognition using Surface Electromyography[END_REF]: For any feature f , f is its frame-based time-domain mean, P f is its frame-based power, and z f is its frame-based zero-crossing rate. S(f, n)

is the stacking of adjacent frames of feature f in the size of 2n + 1 (-n to n) frames.

For an EMG signal with normalized mean x[n], the nine-point doubleaveraged signal w[k] is defined as

w[n] = 1 9 4 n=-4 v[n], where v[n] = 1 9 4 n=-4 x[n].
The rectified high-frequency signal is r

[n] = |x[n] -w[n]|. Then the TD15
feature is defined as 15), where f2 = [ w, P w , P r , z r , r].

TD15 = S(f2,
Note that [START_REF] Jou | Towards Continuous Speech Recognition using Surface Electromyography[END_REF] and (Wand and Schultz, 2009b) only used a stacking width of 5 frames. On the EMG-PIT corpus, the stacking width of 15 frames gives significantly better results (Wand and Schultz, 2009a). In these computations, we used a frame size of 27 ms and a frame shift of 10 ms. These values are reported as giving optimal results in our earlier work, therefore we adopted the same frame size and shift in the TD15 feature extraction.

The TD15 feature is computed for each of the five electrode channels, then the final feature vector per frame is built by stacking the frame-based features of the five channels. After this procedure, Linear Discriminant Analysis is applied to reduce the dimensionality of the final feature vector from 775 to 32 coefficients per frame.

In order to initialize the EMG phoneme models, we require time alignments for the audible EMG training utterances. We obtain these time alignments by using the acoustic data which has been simultaneously recorded.

These acoustic data are forced-aligned with a Broadcast News (BN) speech recognizer trained with the Janus Recognition Toolkit (JRTk). This HMMbased recognizer uses quintphones with 6000 distributions sharing 2000 codebooks. The baseline performance of this acoustic speech recognizer is 10.2%

Word Error Rate (WER) on the clean speech condition (F0) of the official BN test set [START_REF] Yu | Streamlining the Front End of a Speech Recognizer[END_REF].

After the initial EMG phoneme models have been obtained, four iterations of Viterbi training are performed.

For decoding, we apply a trigram language model trained on Broadcast News data. The testing process consists of a Viterbi decoding followed by a lattice rescoring based on a matrix of word penalty and language model weighting parameters in order to obtain optimal recognition results. We use the batch of speaker-specific audible SPEC utterances as training set, and the audible BASE utterances as testing set. Therefore, in total we have a test set of 28 sessions of 14 speakers, with 10 utterances per speaker with a vocabulary of 101 words. On the test set, the trigram-perplexity of the language model is 24.24. The average Word Error Rate obtained with this baseline speaker-dependent EMG-based recognition system is 47.15%.

Speaker-dependent EMG-based Recognition System

In this section, we report results of speaker-dependent EMG-based speech recognition on the audible sentences of the 14 speakers of the pilot study of the EMG-PIT corpus as described in section 2.1. The basic recognizer setup is the same as described in section 2.2.

Modeling Phonetic Features

In (Wand and Schultz, 2009b) we considered speaker-dependent and speakerindependent phoneme-based EMG recognizers. This means that we regard each frame of the EMG signal as the representation of the beginning, middle, or end state of a phoneme. However, it has been shown in acoustic speech recognition [START_REF] Kirchhoff | Robust Speech Recognition Using Articulatory Information[END_REF] that an acoustic speech recognizer can benefit from additionally modeling phonetic features (PFs), which represent properties of a given phoneme, such as the place or the manner of articulation.

Note that in previous works, i.e. [START_REF] Kirchhoff | Robust Speech Recognition Using Articulatory Information[END_REF][START_REF] Metze | Articulatory Features for Conversational Speech Recognition[END_REF], Phonetic

Features are also called "Articulatory Features". Since the phonetic feature modeling approach does not reflect the movements of the articulators, but rather represents phonetic properties of phonemes, we use the term "Phonetic

Features" (PFs) in our work.

It is empirically shown [START_REF] Kirchhoff | Robust Speech Recognition Using Articulatory Information[END_REF] Fricative, Unvoiced, Front, Plosive}.

Phonetic Features as Additional Knowledge Source

The architecture we employ for the PF-based EMG decoding system is a multi-stream architecture [START_REF] Metze | A Flexible Stream Architecture for ASR Using Articulatory Features[END_REF][START_REF] Jou | Continuous Electromyographic Speech Recognition with a Multi-Stream Decoding Architecture[END_REF], see figure 3. This means that the models draw their emission probabilities not from one single source (or stream), but from various sources. The additional sources correspond to phonetic features, like "Vowel" or "Fricative".

The conventional EMG phoneme-based recognizer contributes as well. The emission probabilities are always modeled with Gaussian Mixtures. In [START_REF] Jou | Continuous Electromyographic Speech Recognition with a Multi-Stream Decoding Architecture[END_REF], the authors presented Word Error Rate results on EMG-based speech recognition with phonetic features and showed that using the PFs yields a relative WER improvement of up to 10% over the conventional phoneme-only model. This system used the middle frames of phonemes to train the phonetic feature classifier, since these were assumed to be more stable than beginning and end frames.

We extended the PF recognition system to model PFs for the beginning, middle, and end states of phonemes. We therefore have in each stream six PF models, modeling the beginning, the middle and the end of a present or absent feature. In addition, each stream has one single model for silence.

Since we currently handle planned speech, we refrained from using additional noise models.

For example, the end of "H" in the word "hello" would be modeled using

• the model "H-e" (end of phoneme "H") in the phoneme stream,

• the model "Alveolar-absent" in the "Alveolar" stream,

• the model "Glottal-present" in the "Glottal" stream,

• the model "Plosive-absent" in the "Plosive" stream,

• the model "Fricative-present" in the "Fricative" stream,

• the model "Vowel-absent" in the "Vowel" stream,

• the model "Front-absent" in the "Front" stream,

• etc.

The final score (i.e. the negative log-likelihood -log p(x|model H-e) =:

Score(model H-e)) of an observation x for the model "H-e" is then computed WER. In the remainder of this article we will refer to relative improvements in order to compare the impact of our different modeling schemes in relation to the performance level, i.e. independent of the absolute word error rates.

Furthermore, we statistically testified the significance of our improvements by applying the Student's t-test for paired measures. In case of the comparison between the phoneme-based baseline system with the context-independent PF system, the significance level α is at 0.7% (0.007). In other words, the chance that the improvement happened by coincidence is only seven in a thousand.

Data-Driven Bundling of Phonetic Features

So far, we used phonetic feature classifiers as secondary sources of knowledge by augmenting the conventional phoneme-based model. While this yields slight improvements over the phoneme-only classifier, our experimental data indicates that the PF classification is not powerful enough to make the phoneme classification obsolete. In particular, increasing the weight of the PF streams beyond 0.04 shows a decreasing performance, which clearly indicates that the PF streams, even when taken together, are not as accurate as the phoneme models.

It was suggested by [START_REF] Frankel | Articulatory Feature Recognition Using Dynamic Bayesian Networks[END_REF]) that one major shortcoming of the Context-Independent PF recognition system is that features are modeled as statistically independent. The independence assumption is not correct since physiologically every phonetic feature is generated by the interplay of various articulators, i.e. the interdependent activity of several facial muscles.

Therefore, modeling the interdependence of phonetic features should help in creating more accurate PF models and thus might improve the recognition performance. However, for EMG signals it is not clear from the start which features depend on each other, so the choice of a good algorithm to find dependencies between features is crucial. Also, we consider it to be important to find those dependencies in a data-driven fashion, i.e. by an algorithmic process, instead of relying on any kind of rules or educated guesses. We call the process of pooling dependent features together feature bundling, since eventually we will end up with a set of PF models which represent bundles of PFs, like "voiced fricative" or "rounded front vowel". Accordingly, we call these models Bundled Phonetic Features (BDPF).

As the data-driven algorithm we opted for a standard decision-tree based clustering approach (Bahl et al., 1991), as it is successfully used in traditional acoustic-based speech recognition to cluster phoneme contexts for contextdependent modeling. This algorithm works by creating a context decision tree that assigns classes of context similarities by asking linguistic questions. In our experiments, the predefined set of questions contained questions about phonetic features of the current phoneme. Examples of these categorical questions are: Is the current phone voiced? or Is the current phone a fricative?. Note that the set of PFs which may occur in these questions consists of about 90 different PFs, i.e. it is not limited to the PFs which actually occur as streams in the multi-stream model.

The context decision tree is created separately for each PF stream, from top to bottom. This means that the initial set of models, such as for the stream "FRICATIVE" consists of six models: namely the beginning, middle and end of a "FRICATIVE" as well as the beginning, middle, and end of "NON-FRICATIVE". Each context question splits one model into two new models. As splitting criterion we used the maximization of the loss of entropy caused by the respective split, calculated over the Gaussian mixture weights. Note that both the models representing the presence and absence of a phonetic feature take part in the splitting process. The process ends when a pre-determined termination condition is met. This condition must be chosen based on the properties of the available data to create a good balance between the accuracy and the trainability of the context-dependent models.

Our termination criterion is that a fixed number of 70 tree leaves for each phonetic feature, corresponding to 70 independent models, is generated for each PF stream. This number was experimentally found to yield optimal recognition results. Note that due to the small number of training utterances, we optimized the parameters for the PF bundling on the test set.

We call the decision tree algorithm described above PF Bundling Algorithm. Figure 4 graphically shows an excerpt of an example tree which may have been generated for the VOICED stream.

The full training process consists of three steps, as follows:

1. A common context-independent EMG recognizer, i.e. the baseline recognizer described in section 2.2, is trained on the given training data.

This recognizer uses both phoneme and PF models, but no PF bundling yet.

2. The PF bundling algorithm is performed for each stream, so that a set of bundled phonetic features (BDPFs) is generated for each stream.

3. Finally, the BDPF EMG recognizer is trained using the models defined in the previous step.

The bundling process is performed on the nine most frequent PFs (see The performance of the resulting system can be seen in figure 5, which gives a breakdown of the word error rates for the baseline system, the Context-Independent PF system and the Bundled PF system. The indices on the horizontal axis have the form Speaker ID -Session ID. The sessions are ordered according to the baseline performance. It can be seen that while context-independent PFs only give a small improvement over the baseline of 47.15% WER to 45.50%, PF clustering drastically reduces the WER to 35.78%, which is a relative improvement of 24.1% compared to the baseline system. Again, the improvement is statistically highly significant.

Bundling of Context-Dependent Phonetic Features

The PF bundling algorithm can be adjusted in various ways. In particular, we can create context-dependent PFs. This means that the models for a given PF not only depend on the presence or absence of other PFs for the current phoneme, but also on the PF's context, i.e. its neighboring phonemes.

This modification is easily done by extending the set of predefined linguistic questions which is used for building the context decision tree with questions for the left and right phoneme contexts (i.e. Is the left context phone a vowel? or Is the right context phone a fricative? ). We refer to this system as "Context-Dependent (CD) Bundled PF system."

From traditional acoustic speech recognition it is known that modeling context-dependent phonemes reduces word error rates by about 30%. We investigated the use of triphones for EMG in (Wand and Schultz, 2009b), which gave a relative improvement of 11.5% for a speaker-independent EMG recognizer trained on about 77 minutes of EMG data. The limited success was certainly partly due to the lack of training data, since context-dependent modeling requires a large amount of data for reliable model estimates. However, modeling context-dependent PFs instead of context-dependent phonemes provides a partial solution to this problem, since due to the overall smaller amount of PF units and thus a better data sharing, we have more training data available for PF-based models than for phoneme models. To investigate the performance of this strategy we ran two experiments:

First, we created a Context-Dependent Bundled PF System as described above. Second, we computed PF models which depended on their left and right contexts, but not on the current value of the other PFs, i.e. were not bundled in the sense of our definition. We consistently call this system Context-Dependent PF System. The context-independent Bundled PF System described in section 3.3 serves as the baseline.

Figure 6 depicts the results of these experiments and shows that by using context-dependent bundled PFs, we can reduce the word error rate by another 12% relative, giving the best average WER of 31.49% so far, again a statistically highly significant improvement. However, if we use contextdependent unbundled PFs, the WER drastically increases to about 42%.

The first result is not very surprising, since using context-dependent bundled PFs essentially means giving the decision-tree clustering algorithm more flexibility. As long as the available data is relatively homogeneous and overfitting of the trained PF bundling is avoided, this approach should always give better results. However, the drop in accuracy when only context-dependency is used, clearly proves that PF bundling plays an important role in capturing the variability of PF representations.

With the context-dependent BDPF system in place, it remains to be investigated why the BDPF-based systems perform so much better than the classical phoneme-based system. In order to do so, we ran a further series of experiments, where we started from the phoneme-based system and then incrementally added the context-dependent BDPF streams, ordered according to the frequency of the underlying PFs. Since we found in the previous experiments that in the optimal BDPF system, the phoneme stream practically receives a weight of zero, in this new experiment we distributed the stream weights equally among the available PF streams. This means that after adding one BDPF stream, this stream received a weight of 1, with two BDPF streams, each received a weight of 0.5, and so on.

The results are charted in figure 7: Without PF streams, we have the baseline performance of 47.15% WER, with one stream, we achieve a WER The phoneme-based system is labeled "none".

of 42.16%, and with two streams, the WER drops to 34.42%. With five streams, the WER is 31.25%, which is essentially the same result as the WER of 31.49% for nine streams.

It can thus be seen that adding just a few streams already yields a large performance improvement. However we can also see that using only one BDPF stream, which is an approach similar to the one described in [START_REF] Yu | Enhanced Tree Clustering with Single Pronunciation Dictionary for Conversational Speech Recognition[END_REF], clearly is not enough: Indeed, the largest performance gain is achieved by adding the second stream, and the further gains up to five streams are still significant.

It should be emphasized that even though the first stream is based on the VOICED/NON-VOICED pair, and the second stream is based on the CONSONANT/NON-CONSONANT pair, and so on, it would be incorrect to say that e.g. the second stream gives the system the ability to distinguish consonants and vowels, since this distinction will also appear in the first stream due to the PF bundling. Rather it appears that the first stream cannot yield the full distinctive power of a larger system since the bundling is not sufficiently fine-grained: As described in section 3.3, there are 70 codebooks in each PF stream, whereas the context-independent phoneme recognizer contains 136 codebooks.

Growing a larger tree in the first stream might partly remedy this problem if enough training data is present, however our initial experiments showed that this is not the case for our corpus, and that 70 codebooks per stream give optimal results on the EMG-PIT corpus.

Besides the structure of the phonetic models, the systems described above differ in the number of parameters (i.e. Gaussian distributions) which have to be trained. stream BDPF system is significantly better than the phoneme-based system, with a performance difference of 10.6% relative.

These experiments suggest that the performance gain achieved by BDPFs is indeed due to the BDPF modeling scheme, and also that with the given constraints in training data size, the multi-stream structure, which has the great advantage of re-using training data for each of the streams, is indeed crucial for success.

As a final experiment in this section, we investigate the word lattice generated by our best recognition system, i. Figure 8 shows the lattice-based word error rates. We achieve a lattice WER of 18.76% compared to the 31.49% first-best error rate of our currently best system. Table 3 summarizes the results of our experiments on the speaker-dependent setup. We started from a baseline of 47.15% word error rate. This is the averaged performance of the speaker dependent systems trained on each of the 14 subjects from the pilot subset of the EMG-PIT database. The context-independent PF system gave a 3.5% relative gain over the baseline. By using the context and by bundling the PFs we achieved a drastic improvement of 7.7% and 14.8% respectively, and

In (Wand and Schultz, 2009b), we applied context-dependent (CD) phoneme modeling to the EMG-based speech recognition task for the first time. The context-dependent phoneme recognizer was based on generalized triphones sharing 600 codebooks, where the triphone clustering was performed with the standard decision tree approach (Bahl et al., 1991) which we also use in a modified form for BDPF clustering (see section 3.3). We applied this recognizer, equipped with the TD15 preprocessing, to the multi-speaker scenario.

Consequently, the performance improved to 56.55% word error rate.

Given the context-dependent phoneme models, we were able to conduct two experiments to investigate in which aspects the context-dependent phonemes and the context-dependent bundled PFs differ. In one experiment, we used the optimal context-dependent BDPF recognizer described in sec- The goal of these two experiments is to establish that context-dependent BDPFs capture at least as much coarticulation information as traditional context-dependent phonemes. This is a logical assumption: Comparing the way the decision tree clustering algorithm (Bahl et al., 1991) phoneme models and to phonetic features, we see that the context-dependent BDPF clustering essentially adds significant flexibility to phoneme context clustering, while retaining all the power of the original algorithm, a fact which has also been described in [START_REF] Yu | Enhanced Tree Clustering with Single Pronunciation Dictionary for Conversational Speech Recognition[END_REF]. The results of these experiments do indeed support this claim. Note that on our corpus, it is necessary to employ a multi-speaker scenario for these experiments, since the amount of training data for any single speaker is too small to allow the training of context-dependent phoneme models for single speakers.

In both multi-speaker experiments, we used the same set of nine PFs which was used in the speaker-dependent experiments. However, due to the larger amount of training data, the BDPF clustering trees were grown to an experimentally determined optimal number of 220 leaf nodes, instead of 70 leaf nodes in the speaker-dependent case.

The detailed results of these experiments are charted in Figure 9. The average results are given in Table 4.

It can be seen that both systems which use context-dependent BDPFs perform significantly better than the context-dependent phoneme system.

This clearly shows that as in the speaker-dependent case, phonetic fea- ture bundling significantly increases the modeling power of the system. We also see that the context-dependent phonemes + BDPF system performs only slighty better than the system with context-independent phonemes + BDPFs. The relative gain of 0.64% is not statistically significant. Moreover, it turns out that the optimal weighting of the phoneme stream, compared to the BDPF streams, is still quite low. This observation strongly suggests that indeed all information which the context-dependent phoneme stream yields is also present in the context-dependent BDPF streams.

Figure 1 :

 1 Figure 1: Overview of electrode positioning and captured facial muscles (muscle chart adapted from (Schünke et al., 2006)). See text for description.

  that a speech recognizer which combines phoneme models and PF models performs better under adverse conditions, like poor signal quality or background noise. While EMG-based speech recognition does not suffer from ambient noise, we face the challenge of other noise artifacts, such as the impact of temperature and humidity on the electrodes, or superposition of muscle activity. Therefore, we investigate in this study the effect of PFs on EMG-based speech recognition. Also, when only a small data set is available, PF models get a more robust parameter estimation: Since a phonetic feature is generally shared by multiple phonemes, we can use the combined training data of these phonemes in order to train a phonetic feature model more reliably than a single phone model. The remainder of this section deals with the effect of modeling phonetic features for EMG-based speech recognition. We use PFs which have binary values: For example, each of the articulation places Glottal, Palatal and Labiodental is a PF that has a value either present or absent. These PFs are directly derived from the phonemes and correspond to the IPA phonological features(International Phonetic Association, 1999). The PFs do intentionally not form an orthogonal set because we want the PFs to benefit from redundant information.

Figure 2 Figure 2 :

 22 Figure2shows PF classification F-scores 2 for different phonetic features, where the features are sorted according to the amount of data which is available to train the present models of these phonetic features. We did not consider the training data amount for the absent models, since in the vast majority of cases, the absent model receives much more training data than

Figure 3 :

 3 Figure 3: The Multi-Stream Phonetic Features Decoding Architecture. The upper part shows how the PFs are obtained from the phonetic information, the lower part shows the weighting of the various information sources.

Figure 4 :

 4 Figure4: Example of a BDPF tree for the VOICED stream. Note that these models only apply to end states of phonemes (begin and middle states have their own BDPF trees, similar to this one). The upper nodes with yellow background are predefined and are also present when context-independent unbundled PFs are used; when BDPF models are used, the BDPF tree is generated from this basis.

Figure 2 )Figure 5 :

 25 Figure2), i.e. {Voiced, Consonant, Vowel, Alveolar, Unround, Fricative, Unvoiced, Front, Plosive}. We decided to give the PF streams identical weights and found that under this condition, the optimal weighting of the PF streams was 0.11 for each of the nine features, while the phoneme stream was factored in by a weight of only 0.01. In other words, recognition accuracy was achieved almost exclusively by the PF classifiers. Further optimization

Figure 6 :

 6 Figure 6: Different Phonetic Feature Bundling Methods: Breakdown of Word Error Rates for the Speaker-Dependent System

Figure 7 :

 7 Figure 7: Word Error Rates for Different Numbers of Context-Dependent BDPF Streams.

tion 3 .

 3 4 to train multi-speaker models. Recall that this recognizer consists of a context-independent phoneme-based recognizer augmented by nine additional acoustic knowledge streams of bundled, context-dependent PFs, as shown in figure3. In a second experiment, we used the same recognizer structure, but used a phoneme stream with context-dependent phoneme models instead of the original context-independent phoneme stream. This contextdependent phoneme stream was modeled according to the context-dependent phoneme-based EMG speech recognizer from(Wand and Schultz, 2009b) (see last paragraph).

Figure 9 :

 9 Figure 9: Combination of BDPFs and Context-Dependent and Context-Independent Phoneme Models in a Multi-Speaker Recognizer: Breakdown of Word Error Rates

Table 1 :

 1 Statistics of the EMG-PIT Multiple Speakers Database

	[min]

  Therefore we compared the total number of Gaussian distributions in the different recognition systems. Note that in the PF-based systems, we must get a much higher number of Gaussians than in the phoneme-based system, since the number is determined automatically during the training process based on the available training data, and in the PF-based systems, each training data sample is indeed used ten times, once for the phoneme stream and once for each of the nine PF streams. The numbers of Gaussians are charted in table 2.

		60.00%
		1-best
		Lattice
		50.00%
		40.00%
	Rate
	Error	30.00%
	Word
		20.00%
		10.00%
		0.00%
		Speaker ID -Session ID
		Figure 8: Lattice-based Word Error Rates (Lattice density=100)
		Comparing the phoneme-based system and the BDPF-based system, we
	can see that if we use only one BDPF stream, both systems have got a
	comparable number of Gaussians. However, as shown in figure 7, the one-

Table 2 :

 2 Number of Gaussians for different systems averaged over SpeakersPFs system. A word lattice is the common output format of speech recogni-

tion systems to provide a memory-efficient representation of a large number of alternative hypotheses. By calculating the lattice-based word error rate of our best system, we get an estimate of how much information is available in our representation of the EMG signal. We investigate a lattice with a density of 100 in our experiments. This means that for an utterance where the reference text contains n words, the lattice pruning retains 100 • n nodes. Each node corresponds to a word of the search vocabulary at a specific position within the utterance.

Table 4 :

 4 Summary of Speaker Independent System Performances (averaged over Speakers)

	is applied to

see http://csl.ira.uka.de/index.php?id=146

With C tp = true positive count, C f p = false positive count, C f n = false negative count, precision P = C tp /(C tp + C f p ), and recall R = C tp /(C tp + C f n ), the (balanced) F-Score is defined as F-Score = 2P R/(P + R).

Acknowledgments

The authors would like to thank Szu-Chen (Stan) Jou for his in-depth support with the initial recognition system, his help with the EMG-PIT collection and the data scripts. We also thank Maria Dietrich for recruiting all subjects and carrying out major parts of the database collection. Her study was supported in part through funding received from the SHRS Research Development Fund, School of Health and Rehabilitation Sciences, University of Pittsburgh.

Bahl, L. R., de Souza, P. V., Gopalakrishnan, P. S., Nahamoo, D., Picheny, M. A., 1991. Decision Trees for Phonological Rules in Continuous Speech.

We refer to this multi-stream architecture as Context-Independent (CI)

PF system and apply it to our corpus. The phoneme-based baseline system, which is described in section 2.2 achieves a word error rate of 47.15% averaged over the 14 speakers. With the context-independent PF system we obtain 45.50% WER. In these experiments, the optimal PF stream weighting was experimentally determined on the test set to be 0.04 for each stream, which leaves a weight of 0.64 for the phoneme stream. The results, including a breakdown for speakers, are given in figure 5.

The decrease from 47.15% to 45.50% WER corresponds to an absolute system improvement of 1.65% WER and a relative improvement of 3

Multi-Speaker Recognition System

Having successfully introduced PF clustering, in this section we report the results of PF clustering for a multi-speaker scenario. All systems described in this section are trained with the combined training data from the 14 speakers of the EMG-PIT pilot study and then tested for each speaker and each session on the respective test set. The baseline system, as before, is the contextindependent phoneme-based recognizer described in section 2.2, which for the multi-speaker scenario yields a WER of 62.15%, averaged over all 28 sessions of the 14 speakers.

Conclusions

In this article we have described the EMG-PIT corpus, a multiple speaker large vocabulary database collection of silent and audible EMG speech recordings. We implemented a new strategy of phonetic feature bundling for modeling coarticulation in EMG-based speech recognition and reported results on speaker-dependent and speaker-independent experimental setups. We could show that the appropriate modeling of the interdependence of phonetic features reduces the word error rate of our baseline system by over 33% relative in the speaker-dependent case, and by about 28% in the speakerindependent system. With this approach we achieved an average word error rate of 31.49% on a 101-word vocabulary task, bringing EMG-based speech recognition within useful range for silent speech applications.


Dr Alexander C Ford

Nicole C Suares

D Floor

Effect of Fibre in Chronic Idiopathic Constipation

Keywords: Large intestine < Organ-based, Constipation < Topics, Motility < Topics, Functional GI diseases < Disease-based Systematic review Chronic idiopathic constipation Soluble fibre Insoluble fibre

come

INTRODUCTION

Chronic idiopathic constipation (CIC) is a common condition affecting the gastrointestinal (GI) tract, with an estimated prevalence of between 4% and 20% in cross-sectional community surveys. [START_REF] Drossman | householder survey of functional gastrointestinal disorders. Prevalence, sociodemography, and health impact[END_REF][START_REF] Stewart | Epidemiology of constipation (EPOC) study in the United States: Relation of clinical subtypes to sociodemographic features[END_REF][START_REF] Talley | Functional constipation and outlet delay: A population-based study[END_REF][START_REF] Wald | A multinational survey of prevalence and patterns of laxative use among adults with self-defined constipation[END_REF] This functional disorder is defined as the infrequent and difficult passage of stools in the absence of any physiological abnormality. [START_REF] Longstreth | Functional bowel disorders[END_REF] The condition is commoner in females, the elderly, and those with lower income, [START_REF] Wald | A multinational survey of prevalence and patterns of laxative use among adults with self-defined constipation[END_REF][START_REF] Everhart | A longitudinal survey of self-reported bowel habits in the United States[END_REF][START_REF] Johanson | Clinical epidemiology of chronic constipation[END_REF] and is associated with reduced quality of life. [START_REF] Wald | The burden of chronic constipation on quality of life: Results of a multinational survey[END_REF] Symptoms of CIC can be difficult to treat. [START_REF] Johanson | Chronic constipation: A survey of the patient perspective[END_REF] Lack of dietary fibre is believed to contribute to constipation, [START_REF] Trowell | Definition of fiber and hypotheses that it is a protective factor in certain diseases[END_REF] and many physicians recommend an increase in fibre intake, along with other lifestyle modifications such as improved hydration, as an initial therapy. Insoluble fibres, such as wheat bran, are thought to exert their effect by accelerating intestinal transit time, thereby increasing stool frequency. [START_REF] Payler | The effect of wheat bran on intestinal transit[END_REF] Psyllium, which is a soluble fibre derived from ground ispaghula husk, is ingested with water to increase stool bulk and increase stool frequency. [START_REF] Marteau | Digestibility and bulking effect of ispaghula husks in healthy humans[END_REF] There have been several randomised controlled trials (RCTs) studying the effects of both soluble and insoluble in individuals with CIC published previously, and current guidelines from national and international Gastroenterology organisations advocate the use of dietary fibre as a first line therapy for constipation. [START_REF]World Gastroenterology Organisation practice guidelines: Constipation[END_REF][START_REF] Locke | AGA technical review on constipation[END_REF] Despite this, there has been no recent summary of all available published RCTs to determine what evidence there is, if any, to support the use of fibre in the treatment of CIC. We have therefore conducted a systematic review to examine this issue.

F

Assessment of Risk of Bias

This was performed independently by two investigators, with disagreements resolved by discussion. By recording the methods used to generate the randomisation schedule and conceal treatment allocation, the level of blinding, the proportion of patients who completed follow-up, whether an intention-to-treat analysis was extractable, and whether there was evidence of selective reporting of outcomes, the risk of bias was assessed as described in the Cochrane handbook. 17

RESULTS

The search strategy identified 3146 citations, of which 14 appeared to be relevant to the systematic review, and were retrieved for further evaluation. Six of these studies were eligible for inclusion (Figure 1). [START_REF] Badiali | Effect of wheat bran in treatment of chronic non-organic constipation. A double-blind controlled trial[END_REF][START_REF] Ashraf | Effects of psyllium therapy on stool characteristics, colon transit and anorectal function in chronic idiopathic constipation[END_REF][START_REF] Fenn | A general practice study of the efficacy of Regulan in functional constipation[END_REF][START_REF] Hongisto | A combination of fibre-rich rye bread and yoghurt containing Lactobacillus GG improves bowel function in women with self-reported constipation[END_REF][START_REF] Lopez Roman | Efecto de la ingesta de un preparado lacteo con fibra dietetica sobre el estrenimiento cronic primario idiopatico[END_REF][START_REF] Nunes | A double-blind trial of a celandin, aloevera and psyllium laxative preparation in adult patients with constipation[END_REF] A formal meta-analysis was not performed due to concerns over the methodological quality and risk of bias of the studies identified. The characteristics of eligible and included trials are summarised in Table 1.Four of the eligible trials used soluble fibre. Of these, three used psyllium, [START_REF] Ashraf | Effects of psyllium therapy on stool characteristics, colon transit and anorectal function in chronic idiopathic constipation[END_REF][START_REF] Fenn | A general practice study of the efficacy of Regulan in functional constipation[END_REF][START_REF] Nunes | A double-blind trial of a celandin, aloevera and psyllium laxative preparation in adult patients with constipation[END_REF] and the fourth used a combination of inulin and maltodextrose. [START_REF] Lopez Roman | Efecto de la ingesta de un preparado lacteo con fibra dietetica sobre el estrenimiento cronic primario idiopatico[END_REF] Two used insoluble fibre, wheat bran in one study, [START_REF] Badiali | Effect of wheat bran in treatment of chronic non-organic constipation. A double-blind controlled trial[END_REF] and rye bread in the other. [START_REF] Hongisto | A combination of fibre-rich rye bread and yoghurt containing Lactobacillus GG improves bowel function in women with self-reported constipation[END_REF] The treatment period ranged from 2 to 8 weeks. No trial was at low risk of bias, and the majority were conducted in tertiary care, and recruited predominantly female patients. None of the trials allowed any active medications for the treatment of constipation to be co-administered to patients, though one study did allow a non-medicated cleansing enema as a rescue therapy for patients who did not have a bowel motion for 8 consecutive days. [START_REF] Badiali | Effect of wheat bran in treatment of chronic non-organic constipation. A double-blind controlled trial[END_REF] Efficacy of soluble fibre in chronic idiopathic constipation

The largest identified RCT was conducted by Fenn et al. weeks of therapy, compared with 2.9 stools per week at baseline, P < 0.05), but no such change with placebo. Patients in the active treatment arm also reported improvements in individual parameters such as stool consistency, pain on defaecation, straining, and sense of complete evacuation compared with baseline, although only the former two differences were statistically significant (P < 0.05 for both).

In a Brazilian trial conducted by Nunes et al., [START_REF] Nunes | A double-blind trial of a celandin, aloevera and psyllium laxative preparation in adult patients with constipation[END_REF] 60 patients were recruited and randomised to either 10g of psyllium per day or placebo for 2 weeks in a double-blind fashion. The primary outcome measure used to define a response to therapy was normalisation of evacuation. This was achieved in 26 (86.7%) of 30 patients receiving psyllium compared with only 9 (30.0%) of 30 patients allocated to placebo (P < 0.001).

Finally, Lopez-Roman and colleagues used 20g of a soluble fibre mixture of inulin and maltodextrin, administered as a dairy preparation, in a placebo-controlled trial conducted in Spain. [START_REF] Lopez Roman | Efecto de la ingesta de un preparado lacteo con fibra dietetica sobre el estrenimiento cronic primario idiopatico[END_REF] In total, 32 patients were randomised, with 15 allocated to fibre and 17 receiving placebo. There were significant reductions in the proportion of patients reporting straining during defaecation, sensation of incomplete evacuation, and sensation of obstruction with soluble fibre (P < 0.001 for all analyses). In addition, the number of days between bowel movements was also significantly reduced (P < 0.001).

Efficacy of insoluble fibre in chronic idiopathic constipation

The first trial reporting the efficacy of insoluble fibre in CIC was conducted in Italy by Badiali et al. [START_REF] Badiali | Effect of wheat bran in treatment of chronic non-organic constipation. A double-blind controlled trial[END_REF] The 24 patients recruited were allocated to receive 20g of bran per day or placebo. As this was a cross-over trial, only the first treatment period is described. Following the first 4-week period of the trial (prior to cross-over) no statistically significant difference in response, defined as having no further straining at stool, occurred with active treatment (5 (55.6%) of 9 patients who reported this at baseline) compared with placebo (2 (28.6%) of 7 patients).

The second RCT carried out by Hongisto and colleagues compared four diets: rye bread with Lactobacillus GG yoghurt, rye bread alone, Lactobacillus GG yoghurt alone, and low fibre bread. [START_REF] Hongisto | A combination of fibre-rich rye bread and yoghurt containing Lactobacillus GG improves bowel function in women with self-reported constipation[END_REF] For the purposes of this systematic review, we only examined outcomes in patients randomised to rye bread alone and those given low fibre bread. The study took place over a 3-week period and recruited a total of 29 female patients in these two arms of the trial. Following the 3-week intervention period, the mean difference in number of stools per day was 0.4 for the patients randomised to rye bread compared with those assigned to low fibre bread (P = 0.001). Difficulty of defaecation was also significantly reduced with rye bread (P < 0.001), and stools were significantly softer (P < 0.001).

Adverse events with fibre

No single study reported total adverse events. One trial reported the number of patients in each trial arm who dropped out due to adverse events (one with psyllium and two with placebo). [START_REF] Fenn | A general practice study of the efficacy of Regulan in functional constipation[END_REF] Ashraf et al. recorded individual adverse events, with 18% of psyllium patients experiencing abdominal pain compared with 0% of placebo patients, but no differences in back pain, bloating, or cramping. [START_REF] Ashraf | Effects of psyllium therapy on stool characteristics, colon transit and anorectal function in chronic idiopathic constipation[END_REF] Finally, there were higher

F

DISCUSSION

This systematic review has summarised all the available published evidence examining the efficacy of fibre in patients with CIC. Despite recommendations from both national and international guidelines that increasing dietary fibre intake should be the first-line step in the management of the condition, [START_REF]World Gastroenterology Organisation practice guidelines: Constipation[END_REF][START_REF] Locke | AGA technical review on constipation[END_REF] the evidence for any benefit of this approach appears limited. We identified only six RCTs, none of which were at low risk of bias, and the majority recruited only a small number of patients.

Overall, soluble fibre may be more effective than placebo in patients with CIC. All four studies examining its efficacy, [START_REF] Ashraf | Effects of psyllium therapy on stool characteristics, colon transit and anorectal function in chronic idiopathic constipation[END_REF][START_REF] Fenn | A general practice study of the efficacy of Regulan in functional constipation[END_REF][START_REF] Lopez Roman | Efecto de la ingesta de un preparado lacteo con fibra dietetica sobre el estrenimiento cronic primario idiopatico[END_REF][START_REF] Nunes | A double-blind trial of a celandin, aloevera and psyllium laxative preparation in adult patients with constipation[END_REF] three of which used psyllium, [START_REF] Ashraf | Effects of psyllium therapy on stool characteristics, colon transit and anorectal function in chronic idiopathic constipation[END_REF][START_REF] Fenn | A general practice study of the efficacy of Regulan in functional constipation[END_REF][START_REF] Nunes | A double-blind trial of a celandin, aloevera and psyllium laxative preparation in adult patients with constipation[END_REF] reported significant improvements in individual symptoms of CIC such as straining or pain on defaecation, stool consistency, sensation of incomplete evacuation, and sensation of obstruction. In addition, mean number of stools per week increased, and number of days between bowel movements was also significantly reduced. However, the two trials examining the effects of insoluble fibre compared with placebo or low fibre bread were conflicting, with one trial demonstrating a significant difference in mean number of stools per day with active therapy, as well as reduced difficulty in defaecation and increased stool softness, [START_REF] Hongisto | A combination of fibre-rich rye bread and yoghurt containing Lactobacillus GG improves bowel function in women with self-reported constipation[END_REF] whilst the other cross-over RCT found no significant difference in response to therapy, defined as reporting no straining at stool, with active treatment versus placebo, [START_REF] Badiali | Effect of wheat bran in treatment of chronic non-organic constipation. A double-blind controlled trial[END_REF] though as the trial contained only 24 patients this may have been due to a lack of power. Adverse events were poorly reported. In one placebo-controlled trial of psyllium abdominal pain was reported more frequently among those assigned to active treatment, [START_REF] Ashraf | Effects of psyllium therapy on stool characteristics, colon transit and anorectal function in chronic idiopathic constipation[END_REF] and in the study using fibre-rich rye bread, the combined symptom score for gastrointestinal side effects was higher in patients taking rye bread compared to those in the placebo arm. We performed this systematic review using rigorous methodology. The comprehensive literature search was conducted separately by two investigators. We included and translated non-English language publications. Data extraction was performed separately, again by two investigators, with discrepancies resolved by consensus. We also used an intention-to-treat analysis, where we assumed all dropouts to be treatment failures, wherever possible. Limitations arise from the quality and reporting of the trials included in the systematic review. None of the trials were at low risk of bias, failing to report the method used to generate the randomisation schedule or conceal allocation in all six cases, meaning that the benefit of fibre in CIC in these RCTs may have been overestimated. [START_REF] Juni | Assessing the quality of controlled clinical trials[END_REF] Duration of therapy was relatively short in most instances, with only one study treating patients for more than 4 weeks. [START_REF] Ashraf | Effects of psyllium therapy on stool characteristics, colon transit and anorectal function in chronic idiopathic constipation[END_REF] There was also great variation in the definitions of improvement with therapy, or primary outcome measurements, making comparisons between the trials difficult. Due to all of the above, we elected not to perform a formal meta-analysis. In addition, only one trial recruited patients from primary care, [START_REF] Fenn | A general practice study of the efficacy of Regulan in functional constipation[END_REF] which is where the majority of patients with CIC will first consult a physician, and therefore the setting in which an increase in dietary fibre intake is most likely to be used as a first therapeutic manoeuvre. The fact that the majority of studies we identified, which were conducted in tertiary care,

will not be generalisable to this situation is therefore of concern. Finally, the patients recruited were predominantly female, meaning that any potential benefit of fibre in males with CIC is even less clear.

There have been systematic reviews examining the efficacy of fibre in CIC published previously. The earliest of these, which performed a meta-analysis, concluded that fibre led to a modest improvement in stool frequency compared with placebo or no treatment, which was of a similar magnitude to that achieved with Suares and Ford 14 of 27 laxatives. [START_REF] Tramonte | The treatment of chronic constipation in adults: A systematic review[END_REF] However, the authors did not restrict their analysis to trials of patients with CIC, but also included RCTs conducted in mixed populations of IBS and CIC patients, patients with diverticular disease, and individuals in nursing homes and psychiatric units. A further meta-analysis that pooled data from placebo-controlled trials of fibre and laxatives together reported that any obvious benefit of the two over placebo could not be established. [START_REF] Jones | Lack of objective evidence of efficacy of laxatives in chronic constipation[END_REF] Finally, a systematic review published in 2005 examined the efficacy of various medical therapies, including soluble and insoluble fibre, in patients with CIC. [START_REF] Ramkumar | Efficacy and safety of traditional medical therapies for chronic constipation: Systematic review[END_REF] The authors included head-to-head trials of fibre versus laxatives as well as placebo-controlled trials. Some of the included RCTs included IBS patients, or were conducted in the elderly and institutionalised, post-hysterectomy patients, and pregnant women. No formal meta-analysis was undertaken, but the authors concluded that there was moderate evidence to support the use of psyllium in CIC, but that evidence for any benefit of insoluble fibre such as wheat bran was poor.

The present systematic review provides an update to these previous studies, with half of the trials we identified being published in the intervening years, [START_REF] Hongisto | A combination of fibre-rich rye bread and yoghurt containing Lactobacillus GG improves bowel function in women with self-reported constipation[END_REF][START_REF] Lopez Roman | Efecto de la ingesta de un preparado lacteo con fibra dietetica sobre el estrenimiento cronic primario idiopatico[END_REF][START_REF] Nunes | A double-blind trial of a celandin, aloevera and psyllium laxative preparation in adult patients with constipation[END_REF] as well as restricting the analysis only to RCTs of fibre versus placebo or no therapy, conducted among patients with true CIC.

Current national and international guidelines recommend the use of fibre in both dietary and supplement form in the early management of constipation. [START_REF]World Gastroenterology Organisation practice guidelines: Constipation[END_REF][START_REF] Locke | AGA technical review on constipation[END_REF] However, it is apparent from the trials identified by this systematic review that there is a relative paucity of high quality evidence to support such an approach. In a recent systematic review and meta-analysis of RCTs, which were generally of higher quality than those identified in the present study, both osmotic and stimulant laxatives, as well as newer agents such as prucalopride and linaclotide, were superior to placebo in the treatment of CIC. [START_REF] Ford | Effect of laxatives and pharmacological therapies in chronic idipathic constipation: Systematic review and meta-analysis[END_REF] Two of the largest and most rigorously designed of the RCTs Suares and Ford 15 of 27 included in this meta-analysis were conducted only very recently and confirmed efficacy of both bisacodyl and sodium picosulphate, two traditional, and perhaps neglected, therapies. [START_REF] Kamm | Stimulant laxatives are effective in chronic constipation: Multicenter, 4-week, double-blind, randomized, placebo-controlled trial of bisacodyl[END_REF][START_REF] Mueller-Lissner | Multicenter, 4-week, double-blind, randomized, placebo-controlled trial of sodium picosulfate in patients with chronic constipation[END_REF] This emphasises the need for trials of similar quality studying the efficacy of fibre in CIC. Another recently published meta-analysis has suggested that psyllium is of benefit in IBS, [START_REF] Ford | Effect of fibre, antispasmodics, and peppermint oil in irritable bowel syndrome: Systematic review and meta-analysis[END_REF] and a high quality RCT that studied the efficacy of both soluble and insoluble fibre in IBS demonstrated that both were better than placebo. [START_REF] Bijkerk | Soluble or insoluble fibre in irritable bowel syndrome in primary care? Randomised placebo controlled trial[END_REF] Some investigators have reported a significant degree of overlap between IBS and CIC, [START_REF] Wong | Inability of the Rome III criteria to distinguish functional constipation from constipation-subtype irritable bowel syndrome[END_REF] suggesting common pathophysiological mechanisms. This, together with the fact that previous studies have demonstrated that transit time in some constipated patients normalises with fibre, [START_REF] Chaussade | Determination of total and segmental colonic transit time in constipated patients. Results in 91 patients with a new simplified method[END_REF] means that fibre supplementation should not be excluded from the management of CIC, particularly as there are likely to be few risks arising from such an approach.

In summary, this systematic review suggests that soluble fibre may be of some benefit in CIC. However, further large trials examining the efficacy of both soluble and insoluble fibre in CIC, that recruit patients from primary care, use validated outcome measures, adhere to the recommendations made by the Rome committee for the design of treatment trials in the functional GI disorders, [START_REF] Of | Design of treatment trials for functional gastrointestinal disorders[END_REF] and that report adverse events fully are required before any firm recommendations for the role of either in the management of the condition can be made.

STATEMENTS OF INTEREST

and 22

Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

23-24

Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12).

23-24

Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

8-11

Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency.

N/A N/A DISCUSSION Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

12 Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).

21

 21

13 Conclusions 26 15 FUNDINGFunding 27

 13261527 Provide a general interpretation of the results in the context of other evidence, and implications for future research.13-Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.

  Suares and Ford 9 of 27 psyllium and 63 placebo patients reporting this symptom at study entry, compared with 11 and 27 at trial completion (P = 0.003). Ashraf et al. enrolled 22 patients with CIC, 19 and randomised them in equal numbers to psyllium or placebo for 8 weeks. They reported a significant increase in mean stool frequency per week following treatment with psyllium (3.8 stools per week after 8

	F o r
	P
	e
	e r
	R
	e
	v i e
	w

[START_REF] Fenn | A general practice study of the efficacy of Regulan in functional constipation[END_REF]

This single-blind trial recruited 201 patients, randomising 104 to psyllium and 97 to placebo during a 2-week treatment period. Overall, 90 (86.5%) patients allocated to psyllium reported an improvement in symptoms, compared with 46 (47.4%) patients receiving placebo (P < 0.001). The authors also reported a reduction in abdominal pain and discomfort with psyllium in those patients reporting these symptoms at baseline, with an improvement in 44 (80.0%) of 55 psyllium patients compared with 27 (64.3%) of 42 receiving placebo (P = 0.035). Finally, straining on defaecation was also reduced with psyllium, with 70

Table 1 -

 1 Characteristics of randomised controlled trials of fibre versus placebo in the treatment of CIC

	Authors' Declaration of Personal Interests:
	Nicole C Suares: none declared. Alexander C. Ford: none declared.

Table 2 -

 2 Summary of results of individual randomised controlled trials of fibre versus placebo in the treatment of CIC Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.RESULTSStudy selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

	Study	Active	Criteria used to define	Number in	Treatment effect	Number in	Treatment effect in
		intervention	response to therapy	fibre arm	in fibre arm	placebo / no	placebo / no therapy
						therapy arm	arm
	Fenn 1986 20	Psyllium	Proportion with an	104	86.5%	97	47.4%
			improvement in global
			symptoms
	Ashraf 1995	Psyllium	Increase in mean stool	11	0.9	11	0.2
	19		frequency per week
	Nunes 2005	Psyllium	Proportion with	30	86.7%	30	30.0%
	23		normalisation of
			evacuation

* 24 patients were randomised, but only 16 reported straining at baseline

F o r P e e r R e v i e w

  Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 8, 23-24 Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

F o r P e e r R e v i e w

Section/topic # Checklist item Reported on page # TITLE Title 1 Identify the report as a systematic review, meta-analysis, or both.

N/A

Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

and 16

Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

5 Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

5

Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

6

Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

6

Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

Risk of bias in individual studies

12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

7

Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means).

N/A

Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I

2 ) for each meta-analysis.

N/A

Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

N/A