Search Results for author: Tatiana Likhomanenko

Found 18 papers, 11 papers with code

Flashlight: Enabling Innovation in Tools for Machine Learning

1 code implementation29 Jan 2022 Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become challenging.

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

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

Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition

no code implementations14 Jun 2021 Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed

In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR).

Frame Speech Recognition

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

2 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 Self-Supervised Learning

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

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

Iterative Pseudo-Labeling for Speech Recognition

1 code implementation19 May 2020 Qiantong Xu, Tatiana Likhomanenko, Jacob Kahn, Awni Hannun, Gabriel Synnaeve, Ronan Collobert

In particular, IPL fine-tunes an existing model at each iteration using both labeled data and a subset of unlabeled data.

Ranked #9 on Speech Recognition on LibriSpeech test-other (using extra training data)

Automatic Speech Recognition Data Augmentation

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

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

1 code implementation17 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-across metric)

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

Speech Recognition

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

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.

General Classification

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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