151 citations · 241 across the 5 of their papers we have counts for
5 papers
Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
Shaoshi Ling, Yuxuan Hu, Shuangbei Qian +5
Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions. Pretrained large language models (…
Improving Pseudo-label Training For End-to-end Speech Recognition Using Gradient Mask
Shaoshi Ling, Chen Shen, Meng Cai +1
In the recent trend of semi-supervised speech recognition, both self-supervised representation learning and pseudo-labeling have shown promising results. In this paper, we propose…
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization
Shaoshi Ling, Yuzong Liu
Recent success in speech representation learning enables a new way to leverage unlabeled data to train speech recognition model. In speech representation learning, a large amount o…
Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition
Shaoshi Ling, Yuzong Liu, Julian Salazar +1
We propose a novel approach to semi-supervised automatic speech recognition (ASR). We first exploit a large amount of unlabeled audio data via representation learning, where we rec…
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language Recognition
Shaoshi Ling, Julian Salazar, Yuzong Liu +1
We introduce BERTphone, a Transformer encoder trained on large speech corpora that outputs phonetically-aware contextual representation vectors that can be used for both speaker an…