6 citations · 33 across the 44 of their papers we have counts for
11 papers · 1 filter
Improving Fast-slow Encoder based Transducer with Streaming Deliberation
Ke Li, Jay Mahadeokar, Jinxi Guo +5
This paper introduces a fast-slow encoder based transducer with streaming deliberation for end-to-end automatic speech recognition. We aim to improve the recognition accuracy of th…
Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities
Andros Tjandra, Nayan Singhal, David Zhang +4
End-to-end multilingual ASR has become more appealing because of several reasons such as simplifying the training and deployment process and positive performance transfer from high…
Factorized Blank Thresholding for Improved Runtime Efficiency of Neural Transducers
Duc Le, Frank Seide, Yuhao Wang +4
We show how factoring the RNN-T's output distribution can significantly reduce the computation cost and power consumption for on-device ASR inference with no loss in accuracy. With…
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition
Suyoun Kim, Ke Li, Lucas Kabela +4
Recently, there has been an increasing interest in two-pass streaming end-to-end speech recognition (ASR) that incorporates a 2nd-pass rescoring model on top of the conventional 1s…
Anchored Speech Recognition with Neural Transducers
Desh Raj, Junteng Jia, Jay Mahadeokar +4
Neural transducers have achieved human level performance on standard speech recognition benchmarks. However, their performance significantly degrades in the presence of cross-talk,…
Learning ASR pathways: A sparse multilingual ASR model
Mu Yang, Andros Tjandra, Chunxi Liu +3
Neural network pruning compresses automatic speech recognition (ASR) models effectively. However, in multilingual ASR, language-agnostic pruning may lead to severe performance drop…
Learning a Dual-Mode Speech Recognition Model via Self-Pruning
Chunxi Liu, Yuan Shangguan, Haichuan Yang +3
There is growing interest in unifying the streaming and full-context automatic speech recognition (ASR) networks into a single end-to-end ASR model to simplify the model training a…
Federated Domain Adaptation for ASR with Full Self-Supervision
Junteng Jia, Jay Mahadeokar, Weiyi Zheng +3
Cross-device federated learning (FL) protects user privacy by collaboratively training a model on user devices, therefore eliminating the need for collecting, storing, and manually…
Deliberation Model for On-Device Spoken Language Understanding
Duc Le, Akshat Shrivastava, Paden Tomasello +4
We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU), where a streaming automatic speech recognition (ASR) model produces the firs…
Streaming parallel transducer beam search with fast-slow cascaded encoders
Jay Mahadeokar, Yangyang Shi, Ke Li +5
Streaming ASR with strict latency constraints is required in many speech recognition applications. In order to achieve the required latency, streaming ASR models sacrifice accuracy…
Neural-FST Class Language Model for End-to-End Speech Recognition
Antoine Bruguier, Duc Le, Rohit Prabhavalkar +7
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transduce…