most citedEfficient Domain Adaptation for Speech Foundation Models

2 citations · 2 across the 8 of their papers we have counts for

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cs.CL2023

Practical Conformer: Optimizing size, speed and flops of Conformer for on-Device and cloud ASR

Rami Botros, Anmol Gulati, Tara N. Sainath +5

Conformer models maintain a large number of internal states, the vast majority of which are associated with self-attention layers. With limited memory bandwidth, reading these from…

cs.CL2023

Massively Multilingual Shallow Fusion with Large Language Models

Ke Hu, Tara N. Sainath, Bo Li +7

While large language models (LLM) have made impressive progress in natural language processing, it remains unclear how to utilize them in improving automatic speech recognition (AS…

cs.CL20232 cited

Efficient Domain Adaptation for Speech Foundation Models

Bo Li, Dongseong Hwang, Zhouyuan Huo +8

Foundation models (FMs), that are trained on broad data at scale and are adaptable to a wide range of downstream tasks, have brought large interest in the research community. Benef…

cs.CL2022

Streaming Intended Query Detection using E2E Modeling for Continued Conversation

Shuo-yiin Chang, Guru Prakash, Zelin Wu +7

In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query.However, speaking queries followed by a hotword each time…

cs.CL2022

Turn-Taking Prediction for Natural Conversational Speech

Shuo-yiin Chang, Bo Li, Tara N. Sainath +4

While a streaming voice assistant system has been used in many applications, this system typically focuses on unnatural, one-shot interactions assuming input from a single voice qu…

cs.CL2022

Improving Deliberation by Text-Only and Semi-Supervised Training

Ke Hu, Tara N. Sainath, Yanzhang He +4

Text-only and semi-supervised training based on audio-only data has gained popularity recently due to the wide availability of unlabeled text and speech data. In this work, we prop…