5 papers
Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models
Tsendsuren Munkhdalai, Youzheng Chen, Khe Chai Sim +3
Parameter efficient adaptation methods have become a key mechanism to train large pre-trained models for downstream tasks. However, their per-task parameter overhead is considered…
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models
Rohit Prabhavalkar, Zhong Meng, Weiran Wang +7
The accuracy of end-to-end (E2E) automatic speech recognition (ASR) models continues to improve as they are scaled to larger sizes, with some now reaching billions of parameters. W…
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
Weiran Wang, Zelin Wu, Diamantino Caseiro +10
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scena…
Improving Speech Recognition for African American English With Audio Classification
Shefali Garg, Zhouyuan Huo, Khe Chai Sim +11
Automatic speech recognition (ASR) systems have been shown to have large quality disparities between the language varieties they are intended or expected to recognize. One way to m…
Augmenting conformers with structured state-space sequence models for online speech recognition
Haozhe Shan, Albert Gu, Zhong Meng +3
Online speech recognition, where the model only accesses context to the left, is an important and challenging use case for ASR systems. In this work, we investigate augmenting neur…