Anthony Larcher
email: anthony.larcher@univ-avignon.fr

Jean-Francois Bonastre
email: jean-francois.bonastre@univ-avignon.fr

John S D Mason

Constrained Temporal Structure for Text-Dependent Speaker Verification

Keywords: Speaker recognition, Text-dependent, Password, Embedded application

In the context of mobile devices, speaker recognition engines may suffer from ergonomic constraints and limited amount of computing resources. Even if they prove their efficiency in classical contexts, GMM/UBM systems show their limitations when restricting the quantity of speech data. In contrast, the proposed GMM/UBM extension addresses situations characterised by limited enrolment data and only the computing power typically found on modern mobile devices. A key contribution comes from the harnessing of the temporal structure of speech using client-customised pass-phrases and new Markov model structures. Additional temporal information is then used to enhance discrimination with Viterbi decoding, increasing the gap between client and imposter scores. Experiments on the MyIdea database are presented with a standard GMM/UBM configuration acting as a benchmark. When imposters do not know the client pass-phrase, a relative gain of up to 65% in terms of EER is achieved over the GMM/UBM baseline configuration. The results clearly highlight the potential of this new approach, with a good balance between complexity and recognition accuracy.

Introduction

The speech signal offers several advantages over other biometric signals, with distinct benefits coming from the potential to link together information derived from the context and the content of the message as well as the voice biometric itself. With appropriate classification and fusion, these components can be brought together to enhance any biometric validation process.

There are however practical constraints within the cell-phone scenario, stemming largely from ergonomic factors and the available computing resources typically found within such hand-held devices. Such embedded applications impose constraints and an important one in terms of recognition performance can be the quantity of data, particularly for reference models but also for the subsequent test phase. In certain applications these quantities might prove to have a critical influence on recognition accuracy. Examples include security systems that might well have speech of only a few words spanning just 2 or 3 seconds. The main contributions of this paper address these issues with new computational structures designed to harness maximum information from the temporal structure information (TSI) of speech to reinforce the acoustic modelling.

Classical speaker recognition engines offer a high level of performance as shown for example during NIST evaluations [START_REF] Martin | NIST 2008 speaker recognition evaluation: performance across telephone and room microphone channels[END_REF]. However such systems, which are invariably founded on the GMM/UBM paradigm [START_REF] Bimbot | A tutorial on text-independent speaker verification[END_REF], exhibit high sensitivity to the quantity of data, particularly the reference model data [START_REF] Fauve | Influence of task duration in text-independent speaker verification[END_REF], [START_REF] Fauve | Improving the performance of textindependent short duration SVM-and GMM-based speaker verification[END_REF], [START_REF] Vogt | Minimising speaker verification utterance length through confidence based early verification decisions[END_REF]. Their performance degrades strongly while reducing the duration of speech material available [START_REF] Kanagasundaram | PLDA based Speaker Recognition on Short Utterances[END_REF][START_REF] Stadelmann | Dimension-Decoupled Gaussian Mixture Model for Short Utterance Speaker Recognition[END_REF][START_REF] Nosratighods | A segment selection technique for speaker verification[END_REF]. For situations where the speech duration is below 30 seconds, recognition performance falls rapidly [START_REF] Fauve | Tackling variabilities in speaker verification with a focus on short durations[END_REF][START_REF] Kanagasundaram | Ivector Based Speaker Recognition on Short Utterances[END_REF]. Textdependency is well known to compensate for the lack of data by constraining the acoustic content of the spoken utterance [START_REF] Hébert | Text-dependent speaker recognition[END_REF].

Meaningful comparison of recognition accuracy in text-dependent speaker verification tends to be very difficult due to the lack of controlled evaluations and large scale databases, essential particularly when error rates are very low [START_REF] Doddington | Speaker recognition evaluation methodology-an overview and perspective[END_REF]. Hence the tendency of the community towards the text-independent scenario that benefits from the NIST large scale databases and the independent evaluations [START_REF] Martin | NIST 2008 speaker recognition evaluation: performance across telephone and room microphone channels[END_REF]. However, two major trends dominate the field of text-dependent speaker verification. Approaches based on dynamic programming have been proposed for tasks where the quantity of speech is limited [START_REF] Furui | Cepstral analysis technique for automatic speaker verification[END_REF][START_REF] Starpert | A segmental mixture model for speaker recognition[END_REF][START_REF] Ramasubramanian | Text-Dependent Speaker-Recognition Using One-Pass Dynamic Programming Algorithm[END_REF][START_REF] Avinash | Exploring subsegmental and suprasegmental features for a text-dependent speaker verification in distant speech signals[END_REF]. They provide a precise modelling of the time constraints but lack the generalization power available with hidden Markov model (HMM) approaches [START_REF] Bonastre | Gaussian dynamic warping (GDW) method applied to text-dependent speaker detection and verification[END_REF]. Indeed, HMM and GMM models which are the most common modelling methods [START_REF] Chen | GMM-UBM for text-dependent speaker recognition[END_REF] are more robust to speaker or environment variabilities and can take advantage of larger amounts of data [START_REF] Hébert | Text-dependent speaker recognition[END_REF]. Depending on the type of application that is targeted, HMMs can be used to model whole sentences [START_REF] Benzeghiba | User-customized password speaker verification using multiple reference and background models[END_REF][START_REF] Larcher | Dual scoring for textdependent speaker verification[END_REF], word-level units [START_REF] Rosenberg | Connected word talker verification using whole word Hidden Markov Models[END_REF][START_REF] Kato | Improved speaker, verification over the cellular phone network using phoneme-balanced and digit-sequence-preserving connected digit patterns[END_REF] or phone-level units [START_REF] Matsui | Concatenated phoneme models for text-variable speaker recognition[END_REF][START_REF] Hebert | Phonetic class-based speaker verification[END_REF]. In addition to the two major approaches that dominate text-dependent speaker verification, text-dependent and text-independent speaker verification often cross-pollinate each other. While there have been several attempts to adapt Support Vector Machines [START_REF] Schmidt | Speaker identification via support vector classifiers[END_REF][START_REF] Dong | Support Vector Machines Based Text Dependent Speaker Verification Using HMM superverctors[END_REF] or i-vector systems [START_REF] Aronowitz | Text-Dependent Speaker Verification Using a Small Development Set[END_REF][START_REF] Larcher | I-vectors in the context of phonetically-constrained short utterances for speaker verification[END_REF] to take advantages of a lexical constraints, others incorporate text-dependent techniques in text-independent applications such as [START_REF] Sturim | Speaker verification using text-constrained Gaussian mixture models[END_REF][START_REF] Stolcke | Speaker Recognition with Region-Constrained MLLR Transforms[END_REF].

In this paper, a classification structure that takes advantage of the temporal structure of the speech utterance is examined. The structure utilises text-dependencies derived from a multilayer classifier illustrated in Figure 1, the foundation of which is the standard GMM/UBM. These first two layers are complemented by a third layer that harnesses temporal structure information extracted from speaker-specific phrases. The approach, described in [START_REF] Larcher | Reinforced temporal structure information for embedded utterance-based speaker recognition[END_REF], [START_REF] Larcher | Short utterance-based video aided speaker recognition[END_REF], [START_REF] Larcher | Constrained Viterbi decoding for embedded user-customised password speaker recognition[END_REF] and further developed in this paper, takes advantage of the temporal structure of pass-phrases, an example of which is "Ce petit canard apprend à nager2 ". In order to model the TSI of such a pass-phrase while achieving statistical modelling from the GMM/UBM, we propose to extend the standard paradigm with an HMM/Viterbi approach. Finite-state models aim to incorporate pass-phrase-based information, like temporal organisation of acoustic features, not otherwise harnessed by classical GMM/UBM approaches.

A key point here is the inclusion of additional temporal information within the finite-state modelling. This additional information is used to constrain the Viterbi decoding in order to enhance discrimination. It does so by using the temporal structure of the given pass-phrase. A set of N classical HMM nodes is arranged in time sequence with the transitions in time from one node to the next controlled first by the normal acoustic features and then by additional temporal information.

The proposed structure is designed specifically to accommodate the use of two such simultaneous synchronous signals. The roles of the two can be clearly separated: first, variants of the conventional GMM/UBM nodes; and second, additional synchronization control of state transitions. Here, the latter comes from the acoustic signal and divides each pass-phrase into segments overarching several states of the HMM, as shown in Figure 2. These overarching segments provide constraints at the lexical level that are in addition to those of the finite-state models and that can be harnessed by the recogniser. We refer to these as lexical constraints.

The approach proposed in this work is related to that of others in the literature. For example in [START_REF] Yoma | Robust speaker verification with state duration modeling[END_REF] Becerra Yoma and Facco Pegoraro constrain the state duration of word-units HMMs. Additional knowledge is included in the HMM topology by training different transition probabilities depending on the position of a given word in the speech segment. In [START_REF] Forsyth | Discriminating observation probability (DOP) HMM for speaker verification[END_REF], speaker dependent semi-continuous HMMs are compared to a reference HMM to produce a discriminative representation of the speaker pronouncing a given pass-phrase. In the two previous works, the use of a background HMM to adapt the speaker dependent models or to model the alternative hypothesis strongly limits the flexibility of the system in terms of lexicon. The architecture proposed in this work takes advantages of the GMM/UBM framework to model the alternative hypothesis and adapt the speaker model and thus, gives more flexibility to the user to choose a specific pass-phrase. Another related work is proposed in [START_REF] Yegnanarayana | Combining evidence from source, suprasegmental and spectral features for a fixed-text speaker verification system[END_REF] where supra-segmental temporal information is used to reinforce the robustness of a Dynamic Time Warping algorithm. By combining the different information sources in a later stage, using a neural network, the system does not take advantage of the temporal synchronization of the different signals as is the case in the work presented here.

Many techniques exist in the literature to compensate for the variabilities due to channel or environment mismatch in the GMM/UBM framework. Some of these techniques like RASTA and Short Term Gaussianization work at the parametrization level [START_REF] Hermansky | RASTA-PLP speech analysis technique[END_REF][START_REF] Alam | Comparative Evaluation of Feature Normalization Techniques for Speaker Verification[END_REF] when others are dedicated to score normalization [START_REF] Wu | Speech recognition, technologies and applications -Normalization and transformation techniques for robust speaker recognition[END_REF][START_REF] Toledano | Phoneme and Sub-Phoneme T-Normalization for Text-Dependent Speaker Recognition[END_REF]. These techniques have not been applied in this article which focus on the advantages of our approach compared to the GMM/UBM. Nevertheless, most of the techniques that have been developed for the GMM/UBM may be applied to our approach and are expected to provide similar improvement.

The overall system architecture is described in Section 2. The impact of the lexical information in constraining the Viterbi decoding is described in Section 3. Section 4 describes the experimental protocol and results. It includes a description of the MyIdea database [START_REF] Dumas | MyIdea-Multimodal biometrics database, description of acquisition protocols[END_REF]. Section 5 summarises the benefits of this approach and presents future work directions.

A three-level acoustic architecture

The architecture presented in Figure 1 is an extension of the standard GMM/UBM paradigm. Throughout the text we refer to this new structure as the Embedded LIA SpkDet3 (EBD) [START_REF] Larcher | ALIZE 3.0 -Open Source Toolkit for State-of-the-Art Speaker Recognition[END_REF]. This architecture is configured to deal with a user-customised speaker recognition task. Each client has a customised pass-phrase, which is unique to that person. Hence some form of text dependency can be harnessed within the speaker recognition system.

Training phase

The two first layers of the EBD consist of a classical GMM/UBM speaker recognition system. The upper layer, a standard universal background model (UBM), aims to model the acoustic speech space. This GMM is built off-line using a suitably large amount of data and the classical EM/ML algorithm [START_REF] Bimbot | A tutorial on text-independent speaker verification[END_REF]. A text-independent speaker-specific GMM (2 nd layer) is then adapted from the UBM for each client speaker with the client data using the EM algorithm and the maximum a posteriori (MAP) criterion [START_REF] Gauvain | Maximum a posteriori estimation for multivariate gaussian mixture observations of Markov chains[END_REF].

Finally, a semi-continuous hidden Markov model (SCHMM) [START_REF] Young | The general use of tying in phoneme-based HMM speech recognisers[END_REF] is used with the goal of harnessing the TSI of the individual pass-phrase. This third layer introduces text-dependency into the client models. In order to initialize the S states of the SCHMM, the pass-phrase is cut into S segments {seg i } of equal length. The training algorithm is then a two step process. During the first step, a GMM is trained for each segment seg i by adapting only the weight parameters of the text-independent client model. For the c th distribution of the GMM, the adapted weight parameter ŵc is given by:

ŵc = α c n c T + (1 -α c )w c γ (1)
where w c is the weight of the c th distribution of the text-independent client model, n c is the occupancy of distribution c, T is the total number of speech frames allocated to the c th distribution and γ assures that the weight parameters of a GMM state sum to one. Finally, α c is given by

α c = n c n c + r (2)
with r a regulation factor empirically determined. The second step consists of running a Viterbi decoding with the current SCHMM on the enrolment data to produce new segments {seg i }. The two steps are repeated until convergence of the segmentation. During this process, the number of states is systematically reduced from a given maximum by removing those states that receive fewer than a pre-set minimum number of frames. The optimal initial number of states and the minimum number of frames per state are experimentally determined. Such a combined system was originally proposed in [START_REF] Bonastre | Gaussian dynamic warping (GDW) method applied to text-dependent speaker detection and verification[END_REF] for speaker recognition and extended to word recognition in [START_REF] Lévy | Mobile phone embedded digit-recognition[END_REF].

Testing phase

During a test, two scores are computed. The first is the conventional GMM/UBM log-likelihood ratio obtained from the text-independent speaker model; the second is from the SCHMM. For the case of the original GMM/UBM, the log-likelihood of a test sequence O = {o t }, t ∈ [1, T ] and a N -distribution GMM is given by:

L(GMM) = 1 T T t=1 log N c=1 γ c N (o t |µ c , Σ c ) (3)
where γ c , µ c and Σ c are respectively the weight, mean vector and covariance matrix of the c th distribution of the GMM and N (µ, Σ) indicates a Gaussian distribution with mean µ and covariance Σ. Similarly, for the case of the SCHMM, the log-likelihood of the test sequence O on the SCHMM is:

L(SCHMM) = 1 T T t=1 log N c=1 γ c t N (o t |µ c , Σ c ) (4)
where γ c t is the weight parameter of the c th distribution of the SCHMM state allocated by the Viterbi decoding at time t. Note that the only difference between the two log-likelihood expressions is the parameter γ that weights the contribution of each distribution. Indeed, all distributions from the text-independent speaker model are shared among model states but their weights are unique for each state. Log-likelihoods computed on the GMM and SCHMM (Eq. 3 and 4) are normalized by using the UBM to form loglikelihood ratios that are linearly combined. The final score for the decision stage is given by:

S = λ × L(SCHMM) + (1 -λ) × L(GMM) -L(UBM) (5)
where L(UBM) is the log-likelihood of the test sequence O on the UBM and λ is a empirically chosen in [0; 1]. In addition to the TSI introduced by the semi-continuous HMM, further information can be added to the system in the form of overarching temporal constraints as illustrated in the lower part of Figure 2 (three segments in this case). In Section 3 we describe such additional constraints.

Complexity

With the given mobile application in mind it is important to consider the complexity of the proposed approach and in particular that of the testing phase; the training is less important since that can be performed offline. We estimate the complexity of the testing phase and make comparison with both a standard GMM/UBM and an equivalent HMM.

The complexity is very much dominated by two tasks, namely the likelihood computation and the Viterbi decoding. The likelihood computation is likely to be dominated by the exponential function, for which an estimation is O(n

1 2 M (n))
where n is the number of digits of precision and M (n) is the complexity of the multiplication algorithm. Other key parameters are the number of acoustic features, T , the dimension D of those features and the number of Gaussian components, C. Thus a reasonable estimate of the computational load would be O(n

1 2 M (n)CDT ).
Complexity of the Viterbi algorithm can be expressed as O(T S 2 ) where S is the number of states in the model.

The computational effort associated with an HMM is dominated largely by two components, namely the likelihood computation and the Viterbi decoding. In our EBD architecture, the likelihood component is reduced to that of the GMM/UBM structure since the states of the SCHMM share all components with the text-independent speaker model of the second layer. Thus additional complexity of our approach compared to that of the GMM/UBM is restricted to the Viterbi decoding only.

In assessing the system complexity, we have examined the likelihood/Viterbi ratio with two approaches, namely complexity estimates and profiling of the program. The first is a theoretical estimate of complexity that leads to likelihood/Viterbi ratio of 32 × n 1 2 M (n). The second relates to the actual computation load and leads to an estimate of a likelihood/Viterbi ratio of 16:1. From the combination of these two observations it could reasonably be concluded that likelihood computation itself dominates overall and thus the additional cost of our approach over the standard GMM/UBM is negligible.

The constrained Viterbi

We reinforce the TSI modelling of the client-specific utterance by using additional temporal information. The bottom layer SCHMM of the EBD architecture is based on a conventional left-to-right topology for which two types of transitions are defined (Figure 3): transitions E that are fixed and set to equiprobability and transitions S, the values of which vary during the decoding. Each transition S, with a default probability of 0, is driven by a discrete event or synchronization point. During the decoding, the value of a transition S turns to 1 when reaching the associated synchronization point and back to 0 once this point is past. This temporal constraint forbids the Viterbi path to go across certain areas of the lattice (areas illustrated on Figures 4(a) and 4(b) by filled states). In this context, the bottom layer of the EBD architecture could be compared to a succession of sub-SCHMMs and the Viterbi path is then computed from one synchronization point to the next with the corresponding sub-SCHMM. The synchronization constraint which is applied in the training and test phase could be linked to an external source, leading to the notion of synchronization between two sources of information. The addition of complementary information coming from the synchronization points could improve the training of SCHMM models. Such an improvement could potentially lead to increased client scores during the testing phase. The second effect, mainly expected when constraining the Viterbi decoding, consists in increasing the discrimination power of the EBD.

The effect of the lexical constraint is illustrated in Figures 4(a) and 4(b). The Viterbi path computed under this constraint is not allowed to pass through the blackened states area. Free zones correspond to the intersection of the segmentations computed on both the training and the testing utterances.

Figure 4(a) shows paths obtained on a same model for two different passphrase occurrences: the occurrence used to train this SCHMM model and one occurrence of the same pass-phrase, pronounced by the client. The temporal structure of a client test utterance is assumed to be close to the temporal structure of the training utterance. With this hypothesis the path computed for the client test utterance goes through the allowed zones only. This is illustrated in Figure 4(a). In this case, the synchronization constraint has no effect on the Viterbi decoding and the resulting score remains high.

Figure 4(b) shows two expected alignment paths for a given imposter test utterance, one with the synchronization constraint (the more central one) and one without the synchronization constraint. The temporal structure of an imposter utterance is assumed to be different from that of the true client pass-phrase structure. The path resulting from the alignment of this utterance on the client pass-phrase SCHMM without applying the lexical constraint is assumed to go through the forbidden area. Thus, the synchronization constraint forces the algorithm to find a path through the allowed zones. This path is not optimal and the imposter score is therefore lower than the one computed without any constraint.

Experiments

This section presents the experiments designed to evaluate the benefits of both the SCHMM extension and the constrained decoding. It includes a description of the evaluation corpus and the experimental protocol.

The MyIdea database

The multi-modal MyIdea database [START_REF] Dumas | MyIdea-Multimodal biometrics database, description of acquisition protocols[END_REF] includes recordings of 30 male speakers, each pronouncing 25 sentences in 3 sessions under controlled acoustic conditions. Twelve of the sentences, common to all speakers and sessions, are used for our experiments 4 . Out of these twelve sentences, ten are approximately three seconds long and will be referred to as 3s sentences, two are 6s long. The ten 3s sentences are used as user-customized pass-phrases.

Experimental protocol

The 30 males of the MyIdea database are separated into two groups of 15 speakers each. The whole recorded material of each group is successively used to train the UBM using the classical EM algorithm [START_REF] Reynolds | Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models[END_REF] while the 15 speakers of the remaining group are used for enrolment and tests. Note that when a group of speakers is dedicated to UBM training, data from these speaker is not used for any other purpose. Due to the lack of data, a jackknifing process is used within the enrolment and test group by successively considering each speaker as a client while each of the 14 other speakers of the group is regarded as an imposters [START_REF] Larcher | Reinforced temporal structure information for embedded utterance-based speaker recognition[END_REF].

Each client GMM (layer two of the EBD architecture) is derived using two 6s sentences and one occurrence of the selected 3s sentence (around 15 seconds of speech in total). The given 3s sentence is also used to train the client pass-phrase SCHMM. Over the two groups of 15 speakers, 900 SCHMM models are trained (Table 1).

A speaker model is trained using one 3s sentence from one of the 3 sessions. The same 3s sentence from the two other sessions of the same speaker is used for target trials, giving 2 target trials per speaker model. This process, applied to the 900 target models obtained after jackknifing, leads to a total of 1,800 target trials over the two groups of speakers.

For each speaker model, imposter tests are computed against the 3s sentences from the others 14 speakers of the same group. Three configurations of imposter tests are considered.

UNKNOWN configuration the linguistic content of the imposter test occurrences is different from that of the material used to train the models. Each client model is compared to three randomly selected 3s sentences (one per session) out of the 9 remaining sentences of each of the 14 imposter speakers. This reflects the condition when an imposter does not know the client pass-phrase.

KNOWN configuration the linguistic content of the imposter test occurrences is the same as that of the material used to train the models. Each client model is compared to the three 3s sentences (one per session) of each of the 14 imposter speakers. This reflects the condition when an imposter knows the client pass-phrase.

ALL configuration the imposter tests are all tests from both the KNOWN and the UNKNOWN configurations above.

For both KNOWN and UNKNOWN configurations, the number of imposter tests is constant across the clients. Moreover, the global number of imposter tests is 37,800 in the two first configurations and 75,600 in the ALL configuration. Further experimental details are presented in [START_REF] Larcher | Reinforced temporal structure information for embedded utterance-based speaker recognition[END_REF].

System configuration

Each file of the database is parametrized into a sequence of 32-dimensional vectors made up of 15 Linear-Frequency Cepstral Coefficients (LFCC), the log-energy and the corresponding ∆ coefficients. An energy labelling is applied to separate speech frames from the non-speech frames and cepstral mean and variance normalization (CMVN) is applied to the remaining frames [START_REF] Viikki | Cepstral domain segmental feature vector normalization for noise robust speech recognition[END_REF].

The number of components in GMMs is fixed to 256 across the EBD architecture. The initial number of states in SCHMM to model the 3s sentences is set to 20 regardless of the sentence and the regulation factor r of the MAP adaptation is set to 14. At the end of the iterative training, over all 3s sentences of the 30 speakers, the average number of states per sentence is just over 19. For comparison, modelling the same ten 3s sentences of the MyIdea database by using non-contextual phone models requires an average of 24.2 states per sentence.

Results

Experiments aim to assess the contributions of the three components, namely the GMM/UBM, the SCHMM and the constrained Viterbi. The GMM/UBM is regarded as the baseline. The experimental results are presented in Table 2. The main advantage expected from the EBD system compared to a classical GMM/UBM is the incorporation of pass-phrase-based information as well as the pass-phrase itself and the relative TSI. This hypothesis is supported by results in Table 2 which shows error rates fall from 2.44% to 1.11% using the SCHMM when imposters do not know the client pass-phrases. When the imposters know the client pass-phrases (KNOWN) the performances of the EBD and GMM/UBM systems are equivalent.

Additionally, Figures 5(a) and 5(b) show that this hypothesis stands for different miss probability and false alarm ratios as the DET curves of the EBD system are well below those of the GMM/UBM when imposters do not know the client pass-phrases but comparable when imposters know the client pass-phrase. In order to evaluate the effect of the lexical constraint, a new experiment is performed using the constrained Viterbi decoding. Here, knowledge of the correct client-specific pass-phrase is used in constraining the alignment. The lexical constraint consists of five synchronization points corresponding to word boundaries extracted from each enrolment and test utterance using the LIA SPEERAL Toolkit [START_REF] Nocera | Phoneme lattice based A* search algorithm for speech recognition[END_REF]. The fixed number of synchronization points and their selection process has been empirically determined and various strategies could be applied in the future to optimize this process. The results of this experiment are presented in the fourth column of Table 2. As expected, the performance of the EBD in the UNKNOWN condition improves when constraining the Viterbi alignment with the lexical synchronization. Indeed, the EER drops by 65% relatively to the baseline GMM/UBM (from 2.44% to 0.84%). Here, imposters do not know the client pass-phrase and pronounce a different utterance whose temporal structure is penalised by the synchronization constraint.

Figure 6-A shows that the client score distributions with and without constraining the Viterbi decoding are essentially unchanged. This might be explained by any potential information coming from a lexical synchronization being highly correlated with the acoustic information already exploited by the SCHMM extension. At the same time, these results show that the constrained Viterbi is effective in that it does not degrade client scores. However, benefits are seen in the case of imposters. Figure 6-B confirms that imposter scores decrease consistently, in terms of the distributions. Furthermore, Figure 7 shows that the Viterbi constraint affects a very large number (95%) of imposter tests. This shows that the synchronization constraint leads to sub-optimal alignment when the temporal structure of the test utterance is different from the training pass-phrase structure.

Conclusions and future work

The approach proposed in this paper addresses the issue of speaker recognition in the challenging context of having only a limited quantity of data during both training and test phases. A new temporal structure combining the GMM/UBM text-independent paradigm and the HMM/Viterbi abilities has been investigated, aimed at harnessing the temporal structure information of user-customised pass-phrases. This new three level architecture, referred to as the EBD, is structured so that constraints can be applied to state transitions.

Experimental results are presented for the baseline GMM/UBM and for the EBD, with and without Viterbi constraints. The performances for the EBD and the EBD constrained show relative improvement of 65%, with EERs from the baseline of 2.44% to 1.11% to 0.84% when imposters utter a passphase other than the correct and client-specific phrase. These results show the benefits of harnessing the temporal structure information in combination with the classical GMM/UBM configuration.

Even if the EBD demonstrates its ability to take advantage of the temporal structure of the speaker pass-phrase, the results remains very similar to those of the GMM/UBM when imposters know the client pass-phrase (see row marked 'KNOWN' in Table 2). In [START_REF] Yoma | Robust speaker verification with state duration modeling[END_REF], Becerra Yoma and Facco Pegoraro conclude that state duration restriction seems not to be relevant in the context of clean speech when imposters know the client pass-phrase but that it can lead to significant improvement in noisy conditions. This conclusion may explain our observation and further experiments have to be conducted in the future on noisy signal to evaluate the impact of a temporal structure for the case where imposters know the client pass-phrase. A deeper analysis of the client-specific temporal information may also help to further optimize the EBD for the KNOWN condition, for example by taking into account intrinsically the state duration information as done in [START_REF] Yoma | Robust speaker verification with state duration modeling[END_REF].

Another source of potential improvement could come from the substitution of the lexical synchronization by a less correlated source of information. Future work will focus on multi-modality by replacing the lexical synchronization with temporal information extracted from the video part of the audio-visual stream to increase the performance of the current approach. Additionally, the use of a second modality into the verification process could be very useful to thwart synthetic playback attack by monitoring the temporal correlation of the two streams of information.

Moreover, given that the first results show the ability of the EBD to take advantage of the linguistic content of speaker-specific pass-phrases, more tests are to be performed to evaluate the performance of the EBD system with more utterance-variability, for example considering the pass-phrase duration. at the University of Avignon and a member of the Institut Universitaire de France. He has been vice president of University of Avignon since December 2008. As a member of the Natural Language Processing Group, he developed his research in speaker characterization and recognition using phonetic, statistic and prosodic information, while teaching and lecturing on various subjects covering computer science, speech processing, audio signal classification and indexing, and biometry. In 2002 he took up a one-year sabbatical stay in Panasonic Speech Technology Laboratory, Santa Barbara, CA. From 2001 to 2004, he was the chairman of AFCP, the French-Speaking Speech Communication Association (currently a regional branch of ISCA). He is President of International Speech Communication Association (ISCA) since September 2011, after he has been ISCA vice president since 2007. He was also the chair of the ISCA Speech and Language Characterization SIG for two years. Jean-Francois Bonastre is IEEE Senior Member.

Dr John Mason:

John is currently an Associate Professor at Swansea University. He received MSc and Ph.D. degrees from the University of Surrey in 1971 and 1974 respectively, joining University of Wales Swansea as a lecturer in May 1973. In 1979 he took up a one-year appointment as a senior research engineer at Hewlett Packard Ltd in South Queensferry and in 1994 he was invited to work on an international project at the Australian National University, Canberra, as a visiting research fellow. From the time of his PhD studies through to today his research interest have focused on digital signal processing. Of particular note is the work done on finding solutions to complex Chebyshev approximations, a long-standing problem. Over the last 20 years his research has revolved around speaker recognition. In this area he has served on the technical committees of many international research meetings.

Figure 1 :

 1 Figure 1: General view of the EBD architecture.

Figure 2 :

 2 Figure 2: General view of the EBD architecture integrating additional TSI in the form of three overarching segments.

Figure 3 :

 3 Figure3: Use of a lexical synchronization in the bottom layer of the EBD architecture constrains the Viterbi decoding and increases the discriminative power of the system. In this example, the synchronization signal consists of 2 discrete points separating 3 overarching segments. Transitions E are fixed and set to equiprobability while transitions S value varies during the decoding.

  Viterbi alignment of client training and test utterances with a lexical constraint applied during the decoding. Alignment of imposter utterance with and without lexical constraint.

Figure 4 :

 4 Figure 4: Schematic drawing of the alignment of acoustic feature sequences for different configurations. Time dimension is represented on the horizontal axis while progress across the HMM model is given by the vertical axis.

Figure 5 :

 5 Figure 5: Detection Error Trade-off curves of the GMM-UBM baseline and EBD system with 20 states per SCHMM, with free or constrained Viterbi alignment. Cases where the imposters do not know the client pass-phrase and pronounce a different utterance (UNKNOWN -condition) and where the imposters know the client pass-phrases (KNOWN -condition)

Figure 6 :

 6 Figure 6: Evolution of the client and imposter text-dependent score distributions when using additional information coming from a lexical synchronization (constrained Viterbi decoding) or no synchronization information (classical Viterbi decoding). Case where imposters do not know the client pass-phrase and pronounce a different utterance (UNKNOWN).

Figure 7 :

 7 Figure 7: Distribution of imposter score differences, between Viterbi decoding with and without lexical constraint, when imposters not know the client pass-phrase and pronounce a different utterance (UNKNOWN).

Table 1 :

 1 Number of GMM speaker models trained per sentence, session, speaker and group for the MyIdea database. Note that the number of SCHMM models (layer three of the EBD architecture) is equal to the number of GMM speaker model given in this table.

		Number of models	Detail
	per 3s sentence	1	1
	per speaker per session	10	1 ×10 sentences
	per speaker	30	(1 × 10) ×3 sessions
	per group	450	(1 × 10 × 3) ×15 speakers
	Total	900	(1 × 10 × 3 × 15) ×2 groups

Table 2 :

 2 EER (%) of a standard GMM/UBM configuration compared to the EBD system with 20 states per SCHMM, with the free or constrained Viterbi alignment.

	Configuration	GMM/UBM baseline	EBD system % EER
		% EER	Free Constrained
	UNKNOWN	2.44	1.11	0.84
	KNOWN	4.00	4.06	4.11
	ALL	3.22	2.83	2.89

This little duck is learning to swim.

Implementation of the EBD is based on the open-source toolkit LIA SpkDet, part of ALIZE, http://alize.univ-avignon.fr

The others 13 sentences differ across speakers and sessions and can thus not be used for text-dependent experiments.


Anne Bonneau
email: anne.bonneau@loria.fr

Impact of fluency and segmental categorization in L2: the case of French final fricatives uttered by German speakers

Keywords: L1/L2 interference, fluency, voicing neutralization, categorization, fricatives, German/French

This study examines the effects of L1/L2 interferences at the segmental level, and of lack of fluency at the sentence level, on the realizations of French final fricatives by German learners. Due to L1/L2 interference, German speakers tend to devoice French final fricatives. A well-known effect of the lack of L2 mastering is the decrease of the speech articulation rate, which lengthens segment average duration. In order to better apprehend the impact of categorization and fluency, we selected four series of consonants from the IFCASL corpus, i.e. voiced and unvoiced fricatives uttered by French native (FF) and German non-native (GF) speakers. The realizations of unvoiced consonants uttered by GF speakers are essentially dependent upon fluency, whereas the realizations of voiced consonants by the same speakers are dependent upon both fluency and categorization. We evaluated a set of acoustic cues related to the voicing distinction -including consonant duration and periodicity-, and submitted the data to a hierarchical clustering analysis (HCPC, from R, FactoMineR package). Results, discussed as a function of speaker's level and prosodic boundaries, confirmed the mutual importance of fluency and segmental categorization on non-native realizations.

Introduction

The pronunciation of a non-native segmental category is influenced by the native phonological system of learners [START_REF] Flege | Production and perception of a novel, secondlanguage phonetic contrast[END_REF]. L1/L2 interferences at the segmental level have been the object of a number of models, including Best's PAM [START_REF] Best | Discrimination of non-native consonant contrasts varying in perceptual assimilation to the listener's native phonological system[END_REF] and Flege's SLM [START_REF] Flege | Second-language speech learning: theory, findings, and problems[END_REF]. The latter model predicts that a new L2 sound is easier to acquire by learners when it is dissimilar to L1 sounds than when it is similar to (at least) one of them. Difficulties for learners come also from phonological phenomena, such as neutralizations. In such cases, they might erroneously apply in L2 a rule present in their native language, e.g. German learners might devoice French voiced final obstruents [START_REF] Bonneau | German non-native realizations of French voiced fricatives in final position of a group of words[END_REF]. Inside sentences or texts, the realizations of non-native segmental categories also depend upon L2 fluency. A wellknown consequence of a lack of fluency is the decrease in articulation rate [7] [8]. This decrease in turn influences the duration of speech segments, which tends to be longer in L2 than in L1 speech. L2 speech is also characterized by the presence of more disfluencies, such as silent and filled pauses, or repairs, than L1 [START_REF] Gut | Non-native Speech: A Corpus-based Analysis of Phonological and Phonetic Properties of L2 English and German[END_REF], probably due to a lack of mastering at various L2 levels (lexicon, syntax, intonation …) [START_REF] Baese-Berk | Speaking rate consistency in native and non-native speakers of English[END_REF]. There is a growing number of works on the influence of prosody on segments [START_REF] Kohler | Communicative Functions Integrate Segments in Prosodies and Prosodies in Segments[END_REF] [10] [START_REF] Mitterer | How does prosody influence speech categorization?[END_REF], and this influence is particularly strong in L2 due to the lack of fluency. The lengthening of speech segments at phrase boundaries, often called "final lengthening", is an example of this influence that is of particular interest in this study. It has been observed in a number of works for various languages and increases with the importance of the prosodic domain [START_REF] Wellmann | How each prosodic boundary cue matters: Evidence from German infants[END_REF] [START_REF] Wightman | Segmental durations in the vicinity of prosodic phrase boundaries[END_REF]. Since the degree of "lengthening" should be relatively important before pauses, the higher number of pauses in L2 speech, mentioned above, should lead to the presence of a higher number of long final segments in L2 than in L1. Hence, L2 segments are affected both by L1/L2 interferences at the segmental level and fluency at a more global level.

In order to investigate the impact of fluency and categorization on L2 segments in sentences, we analyzed the pronunciation of French voiced and unvoiced fricatives in final position of an accentual group, but not in sentence final position, by French and German speakers. In German, the phonological opposition between the voiced and the unvoiced fricative categories is neutralized in final position in favor of the unvoiced series [START_REF] Wiese | The phonology of German[END_REF]. Therefore, German speakers tend to devoice French final voiced fricatives [START_REF] Bonneau | German non-native realizations of French voiced fricatives in final position of a group of words[END_REF]. Voicing, on an articulatory point of view, corresponds to vocal fold vibration and generates periodicity. However, the distinction between voiced and unvoiced categories does not rely only upon the presence or absence of periodicity [START_REF] Lisker | A cross-language study of voicing in initial stops: Acoustical measurements[END_REF]. For fricatives, the duration of the consonant, the relative duration of the preceding vowel, as well as the intensity of the noise play also an important role [START_REF] Jessen | Phonetics and phonology of tense and lax obstruents in German[END_REF]. An error in categorization, due to L1/L2 interference at the segmental level, would have the following acoustic consequences: the realizations of the final French voiced consonants by German non native speakers should be longer, more intense, less periodic, and the vowel preceding the consonant should be shorter than what is observed for native realizations.

Inside sentences, the most obvious impact of (a lack of) fluency on speech segments is the lengthening of the average segment durations, due to a decrease in articulation rate. With respect to our study, this should increase fricative duration, an effect that goes in the same direction than that of an error in categorization. In turn, the increase in fricative duration should lead to a decrease in the amount of periodicity during the noise since it is very hard to maintain periodicity in long fricative noises [START_REF] Fuchs | Temporal and spatial aspects concerning the voicing contrast in alveolar and postalveolar fricatives[END_REF], an effect that also goes in the same direction that an error in categorization. In addition, we should observe more fricatives followed by a pause for non-native than for native speakers, which could again contribute to lengthen average fricative durations. To consider this factor, we have distinguished a number of prosodic contexts in the analysis, including the presence or absence of a pause after the fricative under analysis (section 3).

We selected four series of consonants from the IFCASL corpus [18], i.e. voiced and unvoiced fricatives uttered by French native (FF) and German non-native (GF) speakers. The realizations of unvoiced consonants uttered by GF speakers are dependent upon fluency, whereas the realizations of voiced consonants by the same speakers are dependent upon fluency and categorization. We evaluated a set of acoustic cues related to the voicing distinction, and submitted the data to a hierarchical clustering analysis.

Corpus and Protocol

Our corpus material is a subset of the bilingual IFCASL corpus [18]. We extracted three French sentences (sr01, sr03 and sr07) containing respectively the fricatives /z ᴣ ʃ/ in final position of an accentual group but not in sentence final position, and produced by French and German speakers (hereafter the native and non-native speakers, respectively). The subjects were seated in a quiet room and read the sentences from the screen of a Windows laptop, with a headset microphone (AKG C520) connected to an Audiobox (M Audio Fast track). Fourty German learners of French (23 beginners -A1, A2 and B1 levels-, and 17 advanced -B2 and C1 levels) and fifty-four French native speakers recorded the sentences. Each sentence was produced once by each speaker, which makes a total amount of 282 sentences. Some data have been removed due to spelling mistakes and the acoustical analysis was based upon 270 sentences. The sentences were made up of three noun phrases and a verb phrase in that order: NP1 VP NP2 NP3, where NP1 was a subject, NP2 a direct object and NP3 an adverbial phrase beginning with a preposition. The fricative was at the end of the last word of NP2, was preceded by the vowel /a/ and the preposition (i.e. the word following the fricative) began with the vowels /a/ or /ɑ /. Note that we do not find for the consonant /s/ a sentence with a structure identical to the one described above, which explains the absence of this consonant in our corpus. The sentences were « Mon ami a perdu ses bagages à la gare » ("My friend lost its luggage at the railway station"); « Les élèves doivent cocher la bonne case avec un feutre » ("Pupils have to tick the right box with a felt-tip pen"); and « La piscine est couverte d'une bâche en plastique », ("The swimming pool is covered with a plastic sheet"). The broad transcription of the standard dialect form for the context surrounding the fricative consonant is either /aCV/, /aC#V/, or, although less frequent, /aCə#V/.

Segmentation and acoustic cues

The author has segmented the sentences by hand with Praat. In order to take into account the variation of fricative duration due to the boundary type realized by the speaker at the end of NP2, the fricatives have been classified into four groups. In the first group, (G1), fricatives were directly followed by the initial vowel of the last NP (/aCV/); there was no schwa and no pause between the accentual groups. In the second group, (G2), they were followed by a schwa and a pause (/aCǝ#V/). In the third one, (G3), they were followed by a pause (/aC#V/), and in the fourth one, (G4), essentially observed for German speakers, they were followed by a schwa and the initial vowel (/aCǝV/).

We analyzed the values of five acoustic cues. Two cues were related to duration: (1) the duration of the fricative, in milliseconds (DurC), (2) the ratio of the duration of the preceding vowel to that of the consonant (DurVC). The duration values were provided by segmentation. Two cues were related to the intensity of the consonant (1) Low-FreqE, in dB, which evaluates the intensity of the consonant in low frequencies (0-1000Hz), with respect to that of the preceding vowel in the same frequency region, and (2) High-FreqE, which evaluates the intensity of the consonant in high frequencies (above 2000Hz), with respect to that of the preceding vowel in the same frequency region. Low-FreqE should have higher values for voiced consonants whereas High-FreqE should have higher values for intense noises (unvoiced consonants). We used Praat for the evaluation of intensity. The fifth parameter, periodicity (VR), is estimated by the percentage of voiced frames over the duration of the consonant, (function "Voicing Report", Praat software). The estimation of periodicity during fricatives, sounds with often intense noises, being especially difficult, we checked the results manually, and corrected them when necessary (there was few corrections, made for obvious mistakes).

In order to estimate the articulation rate, we calculate the average duration of each segment, which is the inverse of the articulation rate (number of speech units -here segments-per second). Note that the pauses are not taken into account to evaluate the articulation rate, contrary to what is done for the speech rate. We choose the segment as the unit to evaluate the articulation rate, and not the syllable, which is more frequently used, for at least two reasons. Firstly, the sentences had relatively different syllable structures (e.g. there is more clusters in sr03 than sr01). Secondly, a segmental unit is more convenient to make comparisons with the frication durations. To evaluate the average length of each segment in a given sentence, we subtract the duration of the various pauses from the duration of the sentence, and we divided the result by the number of segments constituting the sentence.

Results

Consonant duration.

Table 1 shows the median values for consonant duration for voiced and unvoiced consonants produced by German and French speakers, as well as their variation as a function of sentences, speakers' levels and boundary types (G1 and G3).

For the sake of simplicity, we choose to present and discuss results for the first and the third boundary types only, since the other types, G2 and G4 (consonants followed by a schwa) contained relatively few data. GF speakers realized more pauses after the fricatives than FF speakers (twice more pauses, on the average, for the four series of fricatives) and beginners more pauses than advanced speakers. We made pairwise comparisons using Wilcoxon rank sum test for the statistical analysis, with Benjamini, Hochberg correction for multiple comparisons. The analysis shows that, for a same group (G1 or G3) and a same voicing feature, voiced or unvoiced, consonants pronounced by native speakers are always significantly shorter than those pronounced by nonnative ones (we adopt a statistical significance level of 0.05).

Overall medians show that the durations of the voiced consonants produced by German speakers (Median at 92 ms) are well above those of voiced consonants produced by French speakers (65 ms), and slightly higher than the unvoiced consonants (88 ms) produced by these native speakers, which seems to be in agreement with an error in categorization due to L1. However, we observe in Table 1 that the three modalities sr03, G3 and beginners (from the sentence, boundary type and level factors, respectively) substantially lengthen the duration of the noises. As a consequence, the values observed for voiced consonants uttered by GF beginners are in general higher than those obained for FF unvoiced consonants in the same context. These differences are striking for the sentence sr03, both for the first and the third groups. This clearly shows that an error in categorization cannot explain alone the long noise durations observed here. In the same vein, results for GF unvoiced consonants (122 ms) are well above those for FF unvoiced consonants (88 ms), whereas there is no problem of categorization in this case.

Such results can be explained by a decrease in articulation rates, often observed for L2 speakers. Table 2 shows the averaged duration of speech segments, as a function of sentence, language, and GF speakers' level. Results show that 1) the averaged segment duration is substantially higher for German speakers than for French speakers, and for beginners than for advanced speakers, which is in agreement with previous work [START_REF] Core | R: A Language and Environment for Statistical Computing[END_REF] and 2) concerning German speakers, there is a drastic difference between sentences, values for sr03 being higher than those for sr01 and sr07. Such a difference was not expected since sentences have similar structures. We believe this is due to the complexity of the sentence, which is somewhat higher in sr03 (there are more segments, the syllable structure is in general more complex…). Let us recall that we find the same kind of differences between the duration of the noises for GF sr01 and GF sr03, a difference which is too high to be explained by a difference in place of articulation (see values for French speakers in Table 1, for comparison). We thus made the hypothesis that the differences between the averaged segment length of the sentences, probably due to sentence complexity, have an impact on noise duration for German speakers.

Figure 1 represents the duration of the voiced and unvoiced consonants pronounced by French native and German nonnative speakers as a function of the averaged segment length of the sentence. We observe that in most cases, long fricative durations are associated with long averaged segment lengths.

For German speakers, we find a positive correlation between the duration of the voiced consonants and the averaged segment length (Pearson coefficient of correlation: 0.62, adjusted Rsquared: 0.38, F-statistic: 47.64 on 1 and 75 DF, p-value: 1.414e-09; results given by the R-software [START_REF] Core | R: A Language and Environment for Statistical Computing[END_REF]). The increase in averaged segment duration, which corresponds to a decrease in articulation rate, and which varies with levels and sentences can thus explain a part of the increase of the noise duration for L2 speakers.

Whole set of acoustic cues and clustering analysis.

We observe for all the cues corresponding to periodicity and noise intensity (VR, Low-FreqE, and High-FreqE) the same phenomena than those reported for the consonant duration (see section 4.1, and Table 1): 1) their values vary as a function of speakers' L1, GF levels, boundary types (G1 or G3) and sentences, and 2) concerning GF voiced consonants, values observed for beginners, G3 and (the sentence) sr03 tend to be more characteristic of an unvoiced fricative than in other contexts. The values for these modalities are sometimes more "extreme" than those of French unvoiced fricatives, which means that the realizations are typical of very long and very intense non-periodic noises. As an example, Table 3 displays the results obtained for the Low-FreqE (energy in low frequencies, see section 3). For GF unvoiced consonants, we also observe that the cue values are often different (more "extreme") than those of French unvoiced consonants.

All the realizations, including data from the boundary types G2 and G4, characterized by the values of all the acoustic cues considered in this study, have been submitted to a hierarchical clustering made on principal components (HCPC, from R, FactoMineR package). This function is an unsupervised method that combines principal components methods, hierarchical clustering and partitioning. The analysis splits the consonants into clusters without any a priori and reveals similitudes and differences between the different realisations. We have limited the number of classes to 4.

The results of the HCPC analysis can be visualized in Fig. 2, which displays the repartition of the four clusters on the axes of the principal component analysis. For each class, the analysis provides statistical data on the quantitative variables, i.e. here the acoustic cues.

The first class (in black in Fig. 2) contains essentially non-native data, both voiced and unvoiced. The fourth of all GF voiced data are in this class, with the half of GF unvoiced data (55%). Almost all the FF unvoiced consonants (95%) are in the second class (in red in Fig. 2), with the other half of GF unvoiced consonants (45%), and more than a third (38%) of the GF voiced consonants. Finally, the third and fourth class are only made up of voiced consonants: 86% of the French voiced consonants are in these classes, and 36% of the German voiced consonants, essentially produced by advanced speakers.

In the first class, made up of GF unvoiced and voiced data, the percentage of voiced frames (VR, mean at 14%), the energy in low frequencies (Low-FreqE, mean at -19 dB) as well as the VC duration ratio (DurVC, mean at 1.07) have all very low values. The energy in high frequencies is high (High-FreqE, mean at 9 dB) and the duration of the consonant very important (mean at 162 ms). When we compared all the clusters, the means obtained for the first cluster represent either the lowest values (Low-FreqE, VR, DurVC), or the highest (duration of the consonant, High-FreqE). There are typical of very long (too long for fluent speech), intense, and non-periodic noises.

The clustering analysis confirms the impact of fluency on GF speakers' realizations. The first cluster contained only relatively few FF unvoiced French consonants, but more than half of the German unvoiced consonants, for which there is no problem in categorization with respect to voicing, and the fourth of GF voiced fricatives, all produced by beginners, and in general followed by a pause. In this group, the consonants have very long noises, partly due to the increase in the averaged segment length of the sentences, as shown in the preceding subsection.

Concluding remarks

The study explores the impact of fluency, in particular the decrease in articulation rate, and categorization in the production of French final voiced fricatives by German learners, in controlled prosodic contexts. Results shows that the values of the various acoustic cues observed for German voiced consonants, cannot be explained only by categorization, since a substantial number of realizations exhibits more "extreme" values than French unvoiced consonants. In particular the consonants are longer and have a more intense noise. A clustering analysis (HCPC) gathers in a same cluster both half of the unvoiced GF consonants and the fourth of GF voiced consonants (i.e. half of the beginners' realizations) which exhibit values typical of very long, intense and aperiodic noises. For German speakers, the impact of fluency at the sentence level is shown by the existence of a positive correlation between the averaged segment length (the inverse of the articulation rate), and the duration of consonants.

The study shows the mutual effects of an error in categorization and fluency in learners' segmental categories, which 1) raises the question of how to judge their performance (is it possible to separate effects due to fluency from effects due to a possible error in categorization?) and 2) leads, at least for beginners to the realization of "strong-accented" speech segments.

Acknowledgments. I am very grateful to Anne Gégout-Petit, Professor at IECL-UL-Nancy, expert in statistics, for her help with regard to the statistical analysis.

Figure 1 Figure 2

 12 Figure 1 Consonant duration (ms) as a function of averaged segment length (ms). French voiced consonants in red, French unvoiced in black, German voiced in blue, German unvoiced in green

Table 1

 1 Median values of consonant duration, in ms, as a function of language, boundary types, and category.

	Values

Table 2

 2

	Averaged segment length, in ms, as a function of
	language and sentence. Values for advanced speakers and
	beginners, in that order, are given between parenthesis
	sr01 (/z/)	sr03 (/ʒ/)	sr07 (/ʃ/)
	L1 74	74	75
	L2 94 (86-107) 112 (90-122) 98 (86-104)

Table 3

 3

	Median values for Low-FreqE as a function of language (L1
	or L2), sentence, and boundary types
	sentence sr01 (/z/)	sr03 (/ʒ/)	sr07 (/ʃ/)
	Group	G1	G3	G1	G3	G1	G3
	L1	-7.7	-12.4 -7.8	-11	-13.7 -16.4
	L2	-10.9 -14.1 -12.3 -16.3 -16.6	-17