most citedGated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion

11 citations · 33 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CL20207 cited

Universal Phone Recognition with a Multilingual Allophone System

Xinjian Li, Siddharth Dalmia, Juncheng Li +8

Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages. Multilingual acoustic models, however, genera…

cs.CL2020

Towards Zero-shot Learning for Automatic Phonemic Transcription

Xinjian Li, Siddharth Dalmia, David R. Mortensen +3

Automatic phonemic transcription tools are useful for low-resource language documentation. However, due to the lack of training sets, only a tiny fraction of languages have phonemi…

cs.CL201911 cited

Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models

Siddharth Dalmia, Abdelrahman Mohamed, Mike Lewis +2

Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2…

cs.CL2019

SANTLR: Speech Annotation Toolkit for Low Resource Languages

Xinjian Li, Zhong Zhou, Siddharth Dalmia +2

While low resource speech recognition has attracted a lot of attention from the speech community, there are a few tools available to facilitate low resource speech collection. In t…

cs.CL2019

Multilingual Speech Recognition with Corpus Relatedness Sampling

Xinjian Li, Siddharth Dalmia, Alan W. Black +1

Multilingual acoustic models have been successfully applied to low-resource speech recognition. Most existing works have combined many small corpora together and pretrained a multi…

eess.AS2019

Cross-Attention End-to-End ASR for Two-Party Conversations

Suyoun Kim, Siddharth Dalmia, Florian Metze

We present an end-to-end speech recognition model that learns interaction between two speakers based on the turn-changing information. Unlike conventional speech recognition models…