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Section: New Results

Uncertainty estimation and exploitation in speech processing

Participants : Emmanuel Vincent, Dominique Fohr, Odile Mella, Denis Jouvet, Agnès Piquard-Kipffer, Dung Tran.

Uncertainty and acoustic modeling

In many real-world conditions, the speech signal is overlapped with noise, including environmental sounds, music, or undesired extra speech. Speech enhancement is useful but insufficient: some distortion remains in the enhanced signal which must be quantified in order not to be propagated to the subsequent feature extraction and decoding stages. The framework of uncertainty decoding assumes that this distortion has a Gaussian distribution and seeks to estimate its covariance matrix [5] . A number of uncertainty estimators and propagators have been proposed for this purpose, which typically operate on diagonal covariance matrices and are based on fixed mathematical approximations or heuristics. We obtained more accurate uncertainty estimates by propagating the full uncertainty covariance matrix and by fusing multiple uncertainty estimators [50] , [51] . Overall, we obtained 18% relative error rate reduction with respect to conventional decoding (without uncertainty), that is about twice as much as the reduction achieved by the best single uncertainty estimator and propagator.

In order to motivate further work by the community, we created a new international evaluation campaign on that topic in 2011: the CHiME Speech Separation and Recognition Challenge [2] . After two successful editions in 2011 and 2013, we started working and collecting a new corpus towards the organization of a third edition to be announced in 2015.

Uncertainty and speech recognition

In the framework of using speech recognition for helping communication with deaf or hard of hearing people in the FUI project Rapsodie (cf. 8.1.5 ), our goal is to find the best way for displaying the speech transcription results. To our knowledge there is no suitable, validated and currently available display of the output of automatic speech recognizer for hard-of-hearing persons, in terms of size, colors and choice of the written symbols. The difficulty comes from the fact that speech transcription results contain recognition errors, which may impact the understanding process. Although the speech recognition system does not know the errors it makes, through the computation of confidence measures, the speech recognizer estimates if a word or a syllable is rather correctly recognized or not (cf. ); hence such information can be used to adjust the display of the transcription results.

We have adopted a two-step process. Firstly, we conducted a feasibility study with three hard-of-hearing persons including written display tests on print media and interviews. Secondly, we set up an experimental protocol with five hard-of-hearing persons. It included comprehension tests of 40 written sentences recorded by a French native speaker video projected onto a screen. We have also conducted parallel interviews. Their analysis revealed: (1) the interest of the participants in the project; (2) their difficulties to read International Phonetic Alphabet; (3) the importance of knowing the context of communication; (4) the need for aid in case of errors of the speech recognition system by emphasing the words that are supposed to be well recognized by the system. At this stage of the experimental period, the best display associates writing in a bold spelling the words that are supposed to be correctly recognized, and writing in a normal font using simplified French phonetics the words that are possibly wrongly recognized (according to their confidence measure). The next step will be to set up another experimental protocol in order to compare the current display in three conditions (written sentences vs written sentences with oral and lip reading vs lip reading only).

Uncertainty and phonetic segmentation

As described below, phonetic segmentation has been studied this year for spontaneous speech and non-native speech. Moreover, some portions (of about 30 secondes) of various speech documents have been manually annotated (checking and correction of an automatic segmentation). In the future this manually annotated data will be used to analyze the accuracy of the automatic segmentation, and also to elaborate measures that estimate the quality of the segmentation.

Alignment with spontaneous speech

Within the ANR ORFEO project (cf. 8.1.2 ), we addressed the problem of the alignment of spontaneous speech. The ORFEO audio files were recorded under various conditions with a large SNR range and contain extra speech phenomena and overlapping speech. We trained several sets of acoustic models and tested different methods to adapt them to the various audio files. For selecting the best acoustic models, we compared the alignment outputs obtained with the different acoustic models by using our tool CoALT and the manually annotated portions described above.

We also designed a new automatic grapheme-phoneme tool to generate the potential pronunciations of words and proper names. For what concerns overlapping speech, among the different orthographic transcripts corresponding to the overlapping area, we determined as the main transcript the one that best matches the audio signal, the others are kept in other tiers (in a Praat TextGrid file) with the same time boundaries.

Alignment with non-native speech

Non-native speech alignment with text is one critical step in computer assisted foreign language learning [3] . The alignment is necessary to analyze the learner’s utterance, in view of providing some prosody feedback (as for example bad duration of some syllables). However, non-native speech alignment with text is much more complicated than native speech alignment. This is due to the pronunciation deviations observed on non-native speech, as for example the replacement of some target language phonemes by phonemes of the mother tongue, as well as errors in the pronunciations. Non-native speech alignment with text is currently studied in the ANR IFCASL project (see 8.1.3 ).

Uncertainty and prosody

A statistical analysis was conducted on a large annotated speech corpus to investigate the links between punctuation and automatically detected prosodic structures. The speech data comes froms radio broadcast news and TV shows, that were manually annotated during French speech transcription evaluation campaigns. These corpora contain more than 3 million words and almost 350,000 punctuation marks. The detection of the prosodic boundaries and of the prosodic structures is based on an automatic approach that integrates little linguistic knowledge and mainly uses the amplitude and the direction of the F0 slopes, as well as phone durations. A first analysis of the occurrences of the punctuation marks, with respect to various sub-corpora, has highlighted the variability among annotators. Then, a detailed analysis of the prosodic parameters with respect to the punctuation marks, whether followed or not by a pause, and of the links between the automatically detected prosodic structures and the manually annotated punctuation marks was conducted [18] .