Search Results for author: Tatiana Likhomanenko

Found 30 papers, 13 papers with code

Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition

no code implementations24 May 2024 Zijin Gu, Tatiana Likhomanenko, He Bai, Erik McDermott, Ronan Collobert, Navdeep Jaitly

Language models (LMs) have long been used to improve results of automatic speech recognition (ASR) systems, but they are unaware of the errors that ASR systems make.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition

no code implementations29 Sep 2023 Andrew Rouditchenko, Ronan Collobert, Tatiana Likhomanenko

Audio-visual speech contains synchronized audio and visual information that provides cross-modal supervision to learn representations for both automatic speech recognition (ASR) and visual speech recognition (VSR).

Audio-Visual Speech Recognition Automatic Speech Recognition +4

Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR

no code implementations22 Sep 2023 Sheikh Shams Azam, Tatiana Likhomanenko, Martin Pelikan, Jan "Honza" Silovsky

In this paper, we start by training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and examining the fundamental considerations that can be pivotal in minimizing the performance gap in terms of word error rate between models trained using FL versus their centralized counterpart.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Stabilizing Transformer Training by Preventing Attention Entropy Collapse

1 code implementation11 Mar 2023 Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin, Dan Busbridge, Jason Ramapuram, Yizhe Zhang, Jiatao Gu, Josh Susskind

We show that $\sigma$Reparam provides stability and robustness with respect to the choice of hyperparameters, going so far as enabling training (a) a Vision Transformer {to competitive performance} without warmup, weight decay, layer normalization or adaptive optimizers; (b) deep architectures in machine translation and (c) speech recognition to competitive performance without warmup and adaptive optimizers.

Automatic Speech Recognition Image Classification +6

Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data

no code implementations20 Dec 2022 Mozhdeh Gheini, Tatiana Likhomanenko, Matthias Sperber, Hendra Setiawan

Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language.

Data Augmentation Pseudo Label +2

Continuous Soft Pseudo-Labeling in ASR

no code implementations11 Nov 2022 Tatiana Likhomanenko, Ronan Collobert, Navdeep Jaitly, Samy Bengio

Continuous pseudo-labeling (PL) algorithms such as slimIPL have recently emerged as a powerful strategy for semi-supervised learning in speech recognition.

speech-recognition Speech Recognition

More Speaking or More Speakers?

no code implementations2 Nov 2022 Dan Berrebbi, Ronan Collobert, Navdeep Jaitly, Tatiana Likhomanenko

We perform a systematic analysis on both labeled and unlabeled data by varying the number of speakers while keeping the number of hours fixed and vice versa.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Continuous Pseudo-Labeling from the Start

no code implementations17 Oct 2022 Dan Berrebbi, Ronan Collobert, Samy Bengio, Navdeep Jaitly, Tatiana Likhomanenko

Nevertheless, these approaches still rely on bootstrapping the ST using an initial supervised learning phase where the model is trained on labeled data alone.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Position Prediction as an Effective Pretraining Strategy

1 code implementation15 Jul 2022 Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram, Dan Busbridge, Tatiana Likhomanenko, Joseph Yitan Cheng, Walter Talbott, Chen Huang, Hanlin Goh, Joshua Susskind

This pretraining strategy which has been used in BERT models in NLP, Wav2Vec models in Speech and, recently, in MAE models in Vision, forces the model to learn about relationships between the content in different parts of the input using autoencoding related objectives.

Position speech-recognition +1

Pseudo-Labeling for Massively Multilingual Speech Recognition

no code implementations30 Oct 2021 Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert

Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems.

speech-recognition Speech Recognition

Word Order Does Not Matter For Speech Recognition

no code implementations12 Oct 2021 Vineel Pratap, Qiantong Xu, Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert

In this paper, we study training of automatic speech recognition system in a weakly supervised setting where the order of words in transcript labels of the audio training data is not known.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

3 code implementations2 Apr 2021 Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli

On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%.

Self-Supervised Learning

Joint Masked CPC and CTC Training for ASR

1 code implementation30 Oct 2020 Chaitanya Talnikar, Tatiana Likhomanenko, Ronan Collobert, Gabriel Synnaeve

Self-supervised learning (SSL) has shown promise in learning representations of audio that are useful for automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Self-training and Pre-training are Complementary for Speech Recognition

3 code implementations22 Oct 2020 Qiantong Xu, Alexei Baevski, Tatiana Likhomanenko, Paden Tomasello, Alexis Conneau, Ronan Collobert, Gabriel Synnaeve, Michael Auli

Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data.

 Ranked #1 on Speech Recognition on LibriSpeech train-clean-100 test-other (using extra training data)

speech-recognition Speech Recognition +1

Rethinking Evaluation in ASR: Are Our Models Robust Enough?

1 code implementation22 Oct 2020 Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Paden Tomasello, Jacob Kahn, Gilad Avidov, Ronan Collobert, Gabriel Synnaeve

Finally, we show that training a single acoustic model on the most widely-used datasets - combined - reaches competitive performance on both research and real-world benchmarks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

SlimIPL: Language-Model-Free Iterative Pseudo-Labeling

no code implementations22 Oct 2020 Tatiana Likhomanenko, Qiantong Xu, Jacob Kahn, Gabriel Synnaeve, Ronan Collobert

We improve upon the IPL algorithm: as the model learns, we propose to iteratively re-generate transcriptions with hard labels (the most probable tokens), that is, without a language model.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

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).

Decoder speech-recognition +1

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

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

Language Modelling speech-recognition +1

Who Needs Words? Lexicon-Free Speech Recognition

no code implementations9 Apr 2019 Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert

Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words.

speech-recognition Speech Recognition

InfiniteBoost: building infinite ensembles with gradient descent

1 code implementation4 Jun 2017 Alex Rogozhnikov, Tatiana Likhomanenko

In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas.

BIG-bench Machine Learning General Classification +1

Inclusive Flavour Tagging Algorithm

no code implementations24 May 2017 Tatiana Likhomanenko, Denis Derkach, Alex Rogozhnikov

The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of $B$ mesons in any proton-proton experiment.

TAG

Reproducible Experiment Platform

1 code implementation1 Oct 2015 Tatiana Likhomanenko, Alex Rogozhnikov, Alexander Baranov, Egor Khairullin, Andrey Ustyuzhanin

Data analysis in fundamental sciences nowadays is an essential process that pushes frontiers of our knowledge and leads to new discoveries.

Data Analysis, Statistics and Probability

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