261 citations · 571 across the 16 of their papers we have counts for
17 papers
Enhanced Residual SwinV2 Transformer for Learned Image Compression
Yongqiang Wang, Feng Liang, Haisheng Fu +3
Recently, the deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. However, a challenge of man…
AudioPaLM: A Large Language Model That Can Speak and Listen
Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen +27
We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and…
FastLTS: Non-Autoregressive End-to-End Unconstrained Lip-to-Speech Synthesis
Yongqi Wang, Zhou Zhao
Unconstrained lip-to-speech synthesis aims to generate corresponding speeches from silent videos of talking faces with no restriction on head poses or vocabulary. Current works mai…
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, Daniel S. Park, Wei Han +23
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a m…
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition
Ching-Feng Yeh, Yongqiang Wang, Yangyang Shi +4
Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition. One m…
Streaming Simultaneous Speech Translation with Augmented Memory Transformer
Xutai Ma, Yongqiang Wang, Mohammad Javad Dousti +2
Transformer-based models have achieved state-of-the-art performance on speech translation tasks. However, the model architecture is not efficient enough for streaming scenarios sin…
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications
Yongqiang Wang, Yangyang Shi, Frank Zhang +4
In this paper, we summarize the application of transformer and its streamable variant, Emformer based acoustic model for large scale speech recognition applications. We compare the…
Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition
Yangyang Shi, Yongqiang Wang, Chunyang Wu +5
This paper proposes an efficient memory transformer Emformer for low latency streaming speech recognition. In Emformer, the long-range history context is distilled into an augmente…
Weak-Attention Suppression For Transformer Based Speech Recognition
Yangyang Shi, Yongqiang Wang, Chunyang Wu +5
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR). However, adjacent acousti…
Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
Chunyang Wu, Yongqiang Wang, Yangyang Shi +2
Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and sequence-to-sequence speech recogni-tion. However, it requires access to the full sequence, and…
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces
Frank Zhang, Yongqiang Wang, Xiaohui Zhang +3
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…
Improving N-gram Language Models with Pre-trained Deep Transformer
Yiren Wang, Hongzhao Huang, Zhe Liu +4
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in infe…
Transformer-Transducer: End-to-End Speech Recognition with Self-Attention
Ching-Feng Yeh, Jay Mahadeokar, Kaustubh Kalgaonkar +6
We explore options to use Transformer networks in neural transducer for end-to-end speech recognition. Transformer networks use self-attention for sequence modeling and comes with…
Training ASR models by Generation of Contextual Information
Kritika Singh, Dmytro Okhonko, Jun Liu +8
Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales,…
Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks
Andros Tjandra, Chunxi Liu, Frank Zhang +5
Deep acoustic models typically receive features in the first layer of the network, and process increasingly abstract representations in the subsequent layers. Here, we propose to f…
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
Yongqiang Wang, Abdelrahman Mohamed, Duc Le +10
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional emb…
End-to-end contextual speech recognition using class language models and a token passing decoder
Zhehuai Chen, Mahaveer Jain, Yongqiang Wang +2
End-to-end modeling (E2E) of automatic speech recognition (ASR) blends all the components of a traditional speech recognition system into a unified model. Although it simplifies tr…