6 citations · 33 across the 44 of their papers we have counts for
12 papers · 1 filter
Scaling ASR Improves Zero and Few Shot Learning
Alex Xiao, Weiyi Zheng, Gil Keren +6
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 s…
Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution
Yangyang Shi, Chunyang Wu, Dilin Wang +9
This paper improves the streaming transformer transducer for speech recognition by using non-causal convolution. Many works apply the causal convolution to improve streaming transf…
Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study
Dawei Liang, Yangyang Shi, Yun Wang +6
Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life. Prior studies have shown that leveraging knowledge from a…
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet
Haichuan Yang, Yuan Shangguan, Dilin Wang +6
From wearables to powerful smart devices, modern automatic speech recognition (ASR) models run on a variety of edge devices with different computational budgets. To navigate the Pa…
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric
Suyoun Kim, Duc Le, Weiyi Zheng +6
Measuring automatic speech recognition (ASR) system quality is critical for creating user-satisfying voice-driven applications. Word Error Rate (WER) has been traditionally used to…
Collaborative Training of Acoustic Encoders for Speech Recognition
Varun Nagaraja, Yangyang Shi, Ganesh Venkatesh +3
On-device speech recognition requires training models of different sizes for deploying on devices with various computational budgets. When building such different models, we can be…
Noisy Training Improves E2E ASR for the Edge
Dilin Wang, Yuan Shangguan, Haichuan Yang +6
Automatic speech recognition (ASR) has become increasingly ubiquitous on modern edge devices. Past work developed streaming End-to-End (E2E) all-neural speech recognizers that can…
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios
Jay Mahadeokar, Yangyang Shi, Yuan Shangguan +7
Often, the storage and computational constraints of embeddeddevices demand that a single on-device ASR model serve multiple use-cases / domains. In this paper, we propose aFlexible…
Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency
Yangyang Shi, Varun Nagaraja, Chunyang Wu +9
We propose a dynamic encoder transducer (DET) for on-device speech recognition. One DET model scales to multiple devices with different computation capacities without retraining or…
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding
Suyoun Kim, Abhinav Arora, Duc Le +4
Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator…
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition
Yuan Shangguan, Rohit Prabhavalkar, Hang Su +8
As speech-enabled devices such as smartphones and smart speakers become increasingly ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems th…
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion
Duc Le, Mahaveer Jain, Gil Keren +9
How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area. Previous solutions to this problem were either designed for sp…