35 citations · 59 across the 25 of their papers we have counts for
7 papers · 1 filter
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…
Massive End-to-end Models for Short Search Queries
Weiran Wang, Rohit Prabhavalkar, Dongseong Hwang +11
In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for of…
Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network
Yiling Huang, Weiran Wang, Guanlong Zhao +3
While standard speaker diarization attempts to answer the question "who spoken when", most of relevant applications in reality are more interested in determining "who spoken what".…
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…
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models
Shaan Bijwadia, Shuo-yiin Chang, Weiran Wang +3
Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error…
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…
JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition
Zhong Meng, Weiran Wang, Rohit Prabhavalkar +7
We propose JEIT, a joint end-to-end (E2E) model and internal language model (ILM) training method to inject large-scale unpaired text into ILM during E2E training which improves ra…