no code implementations • 14 Mar 2024 • Frank Zhang, Yibo Zhang, Quan Zheng, Rui Ma, Wei Hua, Hujun Bao, Weiwei Xu, Changqing Zou
Text-driven 3D scene generation techniques have made rapid progress in recent years.
no code implementations • 17 May 2023 • Albert Wong, Steven Whang, Emilio Sagre, Niha Sachin, Gustavo Dutra, Yew-Wei Lim, Gaetan Hains, Youry Khmelevsky, Frank Zhang
Creating accurate predictions in the stock market has always been a significant challenge in finance.
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 • 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 • 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 • 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 • 3 Nov 2020 • Ching-Feng Yeh, Yongqiang Wang, Yangyang Shi, Chunyang Wu, Frank Zhang, Julian Chan, Michael L. Seltzer
Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 27 Oct 2020 • Yongqiang Wang, Yangyang Shi, Frank Zhang, Chunyang Wu, Julian Chan, Ching-Feng Yeh, Alex Xiao
We compare the transformer based acoustic models with their LSTM counterparts on industrial scale tasks.
1 code implementation • 21 Oct 2020 • Yangyang Shi, Yongqiang Wang, Chunyang Wu, Ching-Feng Yeh, Julian Chan, Frank Zhang, Duc Le, Mike Seltzer
For a low latency scenario with an average latency of 80 ms, Emformer achieves WER $3. 01\%$ on test-clean and $7. 09\%$ on test-other.
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 • 18 May 2020 • Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 16 May 2020 • Chunyang Wu, Yongqiang Wang, Yangyang Shi, Ching-Feng Yeh, Frank Zhang
The memory bankstores the embedding information for all the processed seg-ments.
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.
1 code implementation • 23 Oct 2019 • Andros Tjandra, Chunxi Liu, Frank Zhang, Xiaohui Zhang, Yongqiang Wang, Gabriel Synnaeve, Satoshi Nakamura, Geoffrey Zweig
As our motivation is to allow acoustic models to re-examine their input features in light of partial hypotheses we introduce intermediate model heads and loss function.
no code implementations • 22 Oct 2019 • Yongqiang Wang, Abdel-rahman Mohamed, Duc Le, Chunxi Liu, Alex Xiao, Jay Mahadeokar, Hongzhao Huang, Andros Tjandra, Xiaohui Zhang, Frank Zhang, Christian Fuegen, Geoffrey Zweig, Michael L. Seltzer
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition.
Ranked #23 on Speech Recognition on LibriSpeech test-other (using extra training data)