no code implementations • 22 Dec 2022 • Pooja Chitkara, Morgane Riviere, Jade Copet, Frank Zhang, Yatharth Saraf
Speech to text models tend to be trained and evaluated against a single target accent.
no code implementations • 20 Jul 2022 • Laxmi Pandey, Debjyoti Paul, Pooja Chitkara, Yutong Pang, Xuedong Zhang, Kjell Schubert, Mark Chou, Shu Liu, Yatharth Saraf
Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 18 Nov 2021 • Chunxi Liu, Michael Picheny, Leda Sari, Pooja Chitkara, Alex Xiao, Xiaohui Zhang, Mark Chou, Andres Alvarado, Caner Hazirbas, Yatharth Saraf
This paper presents initial Speech Recognition results on "Casual Conversations" -- a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
2 code implementations • 17 Nov 2021 • Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli
On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7. 4 BLEU over 21 translation directions into English.
Ranked #1 on Language Identification on VoxLingua107 (using extra training data)
no code implementations • 10 Nov 2021 • Alex Xiao, Weiyi Zheng, Gil Keren, Duc Le, Frank Zhang, Christian Fuegen, Ozlem Kalinli, Yatharth Saraf, Abdelrahman Mohamed
With 4. 5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 14 Oct 2021 • Sangeeta Srivastava, Yun Wang, Andros Tjandra, Anurag Kumar, Chunxi Liu, Kritika Singh, Yatharth Saraf
While self-supervised speech representation learning has been popular in the speech research community, very few works have comprehensively analyzed audio representation learning for non-speech audio tasks.
Ranked #5 on Audio Classification on Balanced Audio Set
no code implementations • 7 Oct 2021 • Jialu Li, Vimal Manohar, Pooja Chitkara, Andros Tjandra, Michael Picheny, Frank Zhang, Xiaohui Zhang, Yatharth Saraf
Domain-adversarial training (DAT) and multi-task learning (MTL) are two common approaches for building accent-robust ASR models.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 9 Jul 2021 • Xiaohui Zhang, Vimal Manohar, David Zhang, Frank Zhang, Yangyang Shi, Nayan Singhal, Julian Chan, Fuchun Peng, Yatharth Saraf, Mike Seltzer
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 8 Jul 2021 • Andros Tjandra, Diptanu Gon Choudhury, Frank Zhang, Kritika Singh, Alexis Conneau, Alexei Baevski, Assaf Sela, Yatharth Saraf, Michael Auli
Language identification greatly impacts the success of downstream tasks such as automatic speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 14 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).
no code implementations • 5 Apr 2021 • Duc Le, Mahaveer Jain, Gil Keren, Suyoun Kim, Yangyang Shi, Jay Mahadeokar, Julian Chan, Yuan Shangguan, Christian Fuegen, Ozlem Kalinli, Yatharth Saraf, Michael L. Seltzer
How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area.
no code implementations • 11 Feb 2021 • Leda Sari, Kritika Singh, Jiatong Zhou, Lorenzo Torresani, Nayan Singhal, Yatharth Saraf
Although speaker verification has conventionally been an audio-only task, some practical applications provide both audio and visual streams of input.
no code implementations • 9 Nov 2020 • Xiaohui Zhang, Frank Zhang, Chunxi Liu, Kjell Schubert, Julian Chan, Pradyot Prakash, Jun Liu, Ching-Feng Yeh, Fuchun Peng, Yatharth Saraf, Geoffrey Zweig
In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on three popular training criteria: LF-MMI, CTC and RNN-T.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 5 Nov 2020 • Chunxi Liu, Frank Zhang, Duc Le, Suyoun Kim, Yatharth Saraf, Geoffrey Zweig
End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers.
Ranked #15 on Speech Recognition on LibriSpeech test-clean
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 4 Jun 2020 • Mahaveer Jain, Gil Keren, Jay Mahadeokar, Geoffrey Zweig, Florian Metze, Yatharth Saraf
By using an attention model and a biasing model to leverage the contextual metadata that accompanies a video, we observe a relative improvement of about 16% in Word Error Rate on Named Entities (WER-NE) for videos with related metadata.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 19 May 2020 • Frank Zhang, Yongqiang Wang, Xiaohui Zhang, Chunxi Liu, Yatharth Saraf, Geoffrey Zweig
In this work, we first show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classification (CTC) system produces state-of-the-art results.
Ranked #17 on Speech Recognition on LibriSpeech test-other (using extra training data)
no code implementations • 16 May 2020 • Kritika Singh, Vimal Manohar, Alex Xiao, Sergey Edunov, Ross Girshick, Vitaliy Liptchinsky, Christian Fuegen, Yatharth Saraf, Geoffrey Zweig, Abdel-rahman Mohamed
Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems.
no code implementations • 15 May 2020 • Da-Rong Liu, Chunxi Liu, Frank Zhang, Gabriel Synnaeve, Yatharth Saraf, Geoffrey Zweig
Videos uploaded on social media are often accompanied with textual descriptions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 27 Oct 2019 • Kritika Singh, Dmytro Okhonko, Jun Liu, Yongqiang Wang, Frank Zhang, Ross Girshick, Sergey Edunov, Fuchun Peng, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed
Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data.
no code implementations • LREC 2020 • Chunxi Liu, Qiaochu Zhang, Xiaohui Zhang, Kritika Singh, Yatharth Saraf, Geoffrey Zweig
Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations.