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1. The Zero Resource Speech Challenge 2021: Spoken Language Modelling

Author

Ewan Dunbar, Emmanuel Dupoux, Mathieu Bernard, Patricia Rozé, Maureen de Seyssel, Morgane Riviere, Tu Anh Nguyen, Nicolas Hamilakis, Eugene Kharitonov, University of Toronto, Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Apprentissage machine et développement cognitif (CoML), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Facebook AI Research [Paris] (FAIR), Facebook, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-17-EURE-0017,FrontCog,Frontières en cognition(2017), ANR-10-IDEX-0001,PSL,Paris Sciences et Lettres(2010), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de sciences cognitives et psycholinguistique (LSCP), École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL)

Subjects
FOS: Computer and information sciences, Computer Science - Artificial Intelligence, Computer science, 02 engineering and technology, computer.software_genre, Zero-resource, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Standard language, 030507 speech-language pathology & audiology, 03 medical and health sciences, Similarity (psychology), 0202 electrical engineering, electronic engineering, information engineering, Cognitive benchmarks, Computer Science - Computation and Language, business.industry, 020206 networking & telecommunications, computer.file_format, Pipeline (software), Unsupervised speech, Zero (linguistics), Artificial Intelligence (cs.AI), Language modelling, Lowresource, Language model, Artificial intelligence, ABX test, 0305 other medical science, business, Computation and Language (cs.CL), computer, Encoder, Natural language processing, Spoken language
Abstract

We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the Libri-light dataset, which provides up to 60k hours of audio from English audio books without any associated text. We provide a pipeline baseline system consisting on an encoder based on contrastive predictive coding (CPC), a quantizer ($k$-means) and a standard language model (BERT or LSTM). The metrics evaluate the learned representations at the acoustic (ABX discrimination), lexical (spot-the-word), syntactic (acceptability judgment) and semantic levels (similarity judgment). We present an overview of the eight submitted systems from four groups and discuss the main results., Submitted to Interspeech 2021. arXiv admin note: text overlap with arXiv:2011.11588

Published
2021
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2. End-to-End Speech Recognition from the Raw Waveform

Author

Ronan Collobert, Neil Zeghidour, Gabriel Synnaeve, Nicolas Usunier, Emmanuel Dupoux, Université Paris-Saclay, Apprentissage machine et développement cognitif (CoML), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Facebook AI Research [Paris] (FAIR), Facebook, Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IDIAP Research Institute, Laboratoire de sciences cognitives et psycholinguistique (LSCP), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
FOS: Computer and information sciences, Normalization (statistics), Sound (cs.SD), Vocabulary, Computer science, Speech recognition, Pipeline (computing), media_common.quotation_subject, Normalization (image processing), Initialization, Word error rate, 02 engineering and technology, Signal, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science - Sound, [SCCO]Cognitive science, 030507 speech-language pathology & audiology, 03 medical and health sciences, Audio and Speech Processing (eess.AS), 020204 information systems, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Waveform, Index Terms: speech recognition, media_common, Computer Science - Computation and Language, scattering, Filter (signal processing), end-to-end, [SCCO.LING]Cognitive science/Linguistics, waveform, deep, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], gammatones, 0305 other medical science, Computation and Language (cs.CL), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

State-of-the-art speech recognition systems rely on fixed, hand-crafted features such as mel-filterbanks to preprocess the waveform before the training pipeline. In this paper, we study end-to-end systems trained directly from the raw waveform, building on two alternatives for trainable replacements of mel-filterbanks that use a convolutional architecture. The first one is inspired by gammatone filterbanks (Hoshen et al., 2015; Sainath et al, 2015), and the second one by the scattering transform (Zeghidour et al., 2017). We propose two modifications to these architectures and systematically compare them to mel-filterbanks, on the Wall Street Journal dataset. The first modification is the addition of an instance normalization layer, which greatly improves on the gammatone-based trainable filterbanks and speeds up the training of the scattering-based filterbanks. The second one relates to the low-pass filter used in these approaches. These modifications consistently improve performances for both approaches, and remove the need for a careful initialization in scattering-based trainable filterbanks. In particular, we show a consistent improvement in word error rate of the trainable filterbanks relatively to comparable mel-filterbanks. It is the first time end-to-end models trained from the raw signal significantly outperform mel-filterbanks on a large vocabulary task under clean recording conditions., Accepted for presentation at Interspeech 2018

Published
2018
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