532 citations · 2.6k across the 41 of their papers we have counts for
16 papers · 1 filter
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning
Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish +4
We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50…
A Language Agnostic Multilingual Streaming On-Device ASR System
Bo Li, Tara N. Sainath, Ruoming Pang +9
On-device end-to-end (E2E) models have shown improvements over a conventional model on English Voice Search tasks in both quality and latency. E2E models have also shown promising…
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…
A Better and Faster End-to-End Model for Streaming ASR
Bo Li, Anmol Gulati, Jiahui Yu +12
End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for streaming speech recognition [1] across many dimensions, including quality (as measured by…
Cascaded encoders for unifying streaming and non-streaming ASR
Arun Narayanan, Tara N. Sainath, Ruoming Pang +5
End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown competitive performance on several benchmarks. These models are structured to either operate in strea…
FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization
Jiahui Yu, Chung-Cheng Chiu, Bo Li +8
Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. However, emitting fast without degrading quality, as measure…
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, James Qin, Daniel S. Park +5
We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabele…
Parallel Rescoring with Transformer for Streaming On-Device Speech Recognition
Wei Li, James Qin, Chung-Cheng Chiu +2
Recent advances of end-to-end models have outperformed conventional models through employing a two-pass model. The two-pass model provides better speed-quality trade-offs for on-de…
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus
Cal Peyser, Sepand Mavandadi, Tara N. Sainath +3
End-to-end (E2E) automatic speech recognition (ASR) systems lack the distinct language model (LM) component that characterizes traditional speech systems. While this simplifies the…
Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati, James Qin, Chung-Cheng Chiu +8
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (…
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
Wei Han, Zhengdong Zhang, Yu Zhang +6
Convolutional neural networks (CNN) have shown promising results for end-to-end speech recognition, albeit still behind other state-of-the-art methods in performance. In this paper…
Towards Fast and Accurate Streaming End-to-End ASR
Bo Li, Shuo-yiin Chang, Tara N. Sainath +4
End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parame…
Dynamic Sparsity Neural Networks for Automatic Speech Recognition
Zhaofeng Wu, Ding Zhao, Qiao Liang +3
In automatic speech recognition (ASR), model pruning is a widely adopted technique that reduces model size and latency to deploy neural network models on edge devices with resource…
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
Chung-Cheng Chiu, Arun Narayanan, Wei Han +8
In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing wor…
Deliberation Model Based Two-Pass End-to-End Speech Recognition
Ke Hu, Tara N. Sainath, Ruoming Pang +1
End-to-end (E2E) models have made rapid progress in automatic speech recognition (ASR) and perform competitively relative to conventional models. To further improve the quality, a…
A comparison of end-to-end models for long-form speech recognition
Chung-Cheng Chiu, Wei Han, Yu Zhang +11
End-to-end automatic speech recognition (ASR) models, including both attention-based models and the recurrent neural network transducer (RNN-T), have shown superior performance com…