Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Statistical Modeling of Speech

Participants : Antoine Liutkus, Emmanuel Vincent, Irina Illina, Dominique Fohr, Denis Jouvet, Joseph Di Martino, Vincent Colotte, Ken Deguernel, Amal Houidhek, Xabier Jaureguiberry, Aditya Nugraha, Luiza Orosanu, Imran Sheikh, Nathan Souviraà-Labastie, Dung Tran, Imene Zangar, Mohamed Bouallegue, Thibaut Fux, Emad Girgis, Juan Andres Morales Cordovilla, Sunit Sivasankaran, Freha Boumazza.

Source separation

Audio source separation is an inverse problem, which requires the user to guide the separation process using prior models for the source spectra and their spatial covariance matrices. We studied the impact of deterministic subspace constraints [14] over the spatial covariance matrices and pursued our work on the separation of multichannel mixtures guided by multiple, deformed reference signals such as repeated excerpts of the same music or repeated versions of the same sentence uttered by different speakers [17] , [56] . Other models we have been working on include those based on local regularities of the spectral representations of musical sources (KAM, [52] , [43] , [51] ). We also validated the positive impact of speech enhancement based on the FASST toolbox on speaker recognition [53] .

As a new research direction, we extended the Gaussian framework for source separation to the family of α-stable stochastic processes [42] . This extension notably opens the path to new and robust parameters estimation algorithms for source separation [16] , [67] , that should be less prone to local minima. Current research notably comprises multichannel stable processes.

In parallel, we started yet another research track on the use of deep learning for source separation [24] . We proposed a new multichannel enhancement technique that exploits both the spatial properties of the sources as modeled by their spatial covariance matrices and their spectral properties as modeled by a deep neural network [75] . The model parameters are alternately estimated in an expectation-maximization (EM) fashion. We used this technique for music separation and speech enhancement in the context of the 2015 Signal Separation Evaluation Campaign (SiSEC) and the 3rd CHiME Speech Separation and Recognition Challenge, respectively [55] . We also used deep learning to address the fusion of multiple source separation techniques and found it to perform much better than the variational Bayesian model averaging techniques previously investigated [81] .

Finally, we pursued our long-lasting efforts on the evaluation of audio source separation by co-organizing the 2015 Signal Separation Evaluation Campaign (SiSEC) [69] and writing a position paper about the scaling up of dataset sizes [21] .

The ANR young researcher project KAMoulox (2016-2019 - cf. 9.1.5 ), that has just been accepted will deal with large audio archives, and more precisely with the "Archives du CNRS — Musée de l'homme" that gather a large set of old and noisy audio recordings (cf. 4.4 ). The work on source separation can lead to the design of semi automatic denoising and enhancement features, that would allow these researchers to significantly enhance their investigation capabilities, even without expert knowledge in sound engineering.

Acoustic modeling

We explored the use of an auxiliary function technique for fast training of neural networks [58] . We did not apply this technique to deep neural network acoustic models yet.

In the framework of using speech recognition for helping communication with deaf or hard-of-hearing people, robustness of the acoustic modeling was investigated. Studies were related to improving robustness with respect to speech signal level and environment noise through multicondition training and enhanced set of acoustic features (noise robust features or standard features after spectral noise subtraction) [37] .

Linguistic modeling

Out-of-vocabulary proper name retrieval

Recognition of proper names (PN) is a challenging task in information retrieval in large audio/video databases. Proper names are semantically rich and are usually key to understanding the information contained in a document. Within the ContNomina project (cf. 9.1.3 ), we focus on increasing the vocabulary coverage of a speech transcription system by automatically retrieving proper names from contemporary text documents. We proposed methods that dynamically augment the automatic speech recognition system vocabulary, using lexical and temporal features in diachronic documents (documents that evolve over the time). Our work uses temporal context modeling to capture the lexical information surrounding proper names so as to retrieve out-of-vocabulary (OOV) proper names and increase the automatic speech recognition vocabulary.

We proposed new methods to retrieve OOV PNs relevant to an audio news document by using probabilistic topic models. We addressed retrieval of rare OOV PNs, which further improves the recall. Our proposed lexical context model improves the mean average precision of OOV PN retrieval [62] . We also proposed a two step approach for recognition of OOV PNs in an audio document. The first step retrieves OOV PNs relevant to an audio document using probabilistic topic models; and the second step uses a phonetic search for the target OOV PNs using a k-differences approximate string matching algorithm [63] . In [64] , we discuss two specific phenomena, word frequency bias and loss of specificity, which affect the retrieval of OOV PNs using Latent Dirichlet Allocation (LDA) topic models. We studied different entity-topic models, which are extensions of LDA designed to learn relations between words, topics and PNs. We showed that our proposed methods of rare OOV PN and lexical context re-ranking improve the recall and the mean average precision for the LDA and the entity-topic models.

For OOV retrieval, we proposed the continuous space word representation using neural networks. This continuous vector representation (word embeddings) is learned from large amounts of unstructured text data. To model semantic and lexical context of proper names, different strategies of local context modeling were proposed [34] , [33] . We studied OOV PN retrieval using temporal versus topic context modeling, different word representation spaces for word-level and document-level context modeling, and combinations of retrieval results [38] . We extended the previously proposed neural networks for word embedding models: the word vector representation proposed by Mikolov is enriched by an additional non-linear transformation. This model allows to better take into account lexical and semantic word relationships [39] .

Adding words in a language model

A novel approach was proposed to add some new words in an existing n-gram language model, based on a similarity measure between the new words to be added and words already present in the language model [47] . Based on a small set of sentences containing the new words and on a set of n-gram counts containing the known words (known for the current language model), we search for known words which have the most similar neighbor distribution (of the few preceding and few following neighbor words) to the new words. The similar words are determined through the computation of KL divergences on the distribution of neighbor words. The n-gram parameter values associated to the similar words are then used to define the n-gram parameter values of the new words.

Selecting data for training a language model

Large vocabulary language models for speech transcription are usually trained from large amounts of textual data collected from various sources, which are more or less related to the target task. Selecting data that matches the target task was investigated in this context [46] , this leads to a small reduction of the perplexity, and a smaller size of the resulting language model.

Music language modeling

Similarly to speech, music involves several levels of information, from the acoustic signal up to cognitive quantities such as composer style or key, through mid-level quantities such as a musical score or a sequence of chords. The dependencies between mid-level and lower- or higher-level information can be represented through acoustic models and language models, respectively. We pursued our pioneering work on music language modeling, with a particular focus on the modeling of long-term structure [12] . We also assessed the applicability of our prior work on joint modeling of note and chord sequences to new corpora of improvised jazz music, with the difficulty that these corpora are very small.

Speech generation by statistical methods

Pathological voice transformation

With respect to pathological voice processing, a competing approach to signal processing techniques consists in recognizing the pathological voice in order to transform it in a text version that can be re-synthesized. Such an approach is currently being experimented, and preliminary results are quite encouraging [15] .

HMM-based synthesis

This year, we started working on HMM-based synthesis in the framework of a CMCU PHC project with ENIT (Engineer school at Tunis-Tunisia; cf. ). Two topics will be explored by two PhD students. The first topic deals with the building of an Arabic corpora along with the analysis of linguistic features which are relevant for the HMM-based synthesis of the Arabic language. The second topic deals with improving the quality of the HMM-based synthesis system. In parallel, we started applying the HTS system (HMM-based Speech Synthesis System) to the French language.