Search Results for author: Vitaliy Liptchinsky

Found 13 papers, 7 papers with code

Beyond English-Centric Multilingual Machine Translation

7 code implementations21 Oct 2020 Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin

Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages.

Machine Translation Translation

Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters

no code implementations6 Jul 2020 Vineel Pratap, Anuroop Sriram, Paden Tomasello, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

We study training a single acoustic model for multiple languages with the aim of improving automatic speech recognition (ASR) performance on low-resource languages, and over-all simplifying deployment of ASR systems that support diverse languages.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Scaling Up Online Speech Recognition Using ConvNets

no code implementations27 Jan 2020 Vineel Pratap, Qiantong Xu, Jacob Kahn, Gilad Avidov, Tatiana Likhomanenko, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

We design an online end-to-end speech recognition system based on Time-Depth Separable (TDS) convolutions and Connectionist Temporal Classification (CTC).

speech-recognition Speech Recognition

Libri-Light: A Benchmark for ASR with Limited or No Supervision

2 code implementations17 Dec 2019 Jacob Kahn, Morgane Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdel-rahman Mohamed, Emmanuel Dupoux

Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).

 Ranked #1 on Speech Recognition on Libri-Light test-other (ABX-within metric)

speech-recognition Speech Recognition +1

End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures

1 code implementation19 Nov 2019 Gabriel Synnaeve, Qiantong Xu, Jacob Kahn, Tatiana Likhomanenko, Edouard Grave, Vineel Pratap, Anuroop Sriram, Vitaliy Liptchinsky, Ronan Collobert

We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions.

Ranked #15 on Speech Recognition on LibriSpeech test-other (using extra training data)

Language Modelling speech-recognition +1

Fully Convolutional Speech Recognition

no code implementations17 Dec 2018 Neil Zeghidour, Qiantong Xu, Vitaliy Liptchinsky, Nicolas Usunier, Gabriel Synnaeve, Ronan Collobert

In this paper we present an alternative approach based solely on convolutional neural networks, leveraging recent advances in acoustic models from the raw waveform and language modeling.

Language Modelling speech-recognition +1

To Reverse the Gradient or Not: An Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition

no code implementations9 Dec 2018 Yossi Adi, Neil Zeghidour, Ronan Collobert, Nicolas Usunier, Vitaliy Liptchinsky, Gabriel Synnaeve

In multi-task learning, the goal is speaker prediction; we expect a performance improvement with this joint training if the two tasks of speech recognition and speaker recognition share a common set of underlying features.

Multi-Task Learning Speaker Recognition +2

Gated ConvNets for Letter-Based ASR

no code implementations ICLR 2018 Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model.

Language Modelling speech-recognition +1

Letter-Based Speech Recognition with Gated ConvNets

2 code implementations22 Dec 2017 Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

In the recent literature, "end-to-end" speech systems often refer to letter-based acoustic models trained in a sequence-to-sequence manner, either via a recurrent model or via a structured output learning approach (such as CTC).

Language Modelling speech-recognition +1

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