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cs.CL2024
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…
cs.CL2023
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…
cs.CL2023
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…