30 citations · 70 across the 7 of their papers we have counts for
8 papers
Clustering and Mining Accented Speech for Inclusive and Fair Speech Recognition
Jaeyoung Kim, Han Lu, Soheil Khorram +3
Modern automatic speech recognition (ASR) systems are typically trained on more than tens of thousands hours of speech data, which is one of the main factors for their great succes…
Contrastive Siamese Network for Semi-supervised Speech Recognition
Soheil Khorram, Jaeyoung Kim, Anshuman Tripathi +3
This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition. c-siam is the first network that extracts…
Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection
Wei Xia, Han Lu, Quan Wang +4
In this paper, we present a novel speaker diarization system for streaming on-device applications. In this system, we use a transformer transducer to detect the speaker turns, repr…
Reducing Streaming ASR Model Delay with Self Alignment
Jaeyoung Kim, Han Lu, Anshuman Tripathi +2
Reducing prediction delay for streaming end-to-end ASR models with minimal performance regression is a challenging problem. Constrained alignment is a well-known existing approach…
Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition
Anshuman Tripathi, Jaeyoung Kim, Qian Zhang +2
In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model. The mod…
Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss
Qian Zhang, Han Lu, Hasim Sak +4
In this paper we present an end-to-end speech recognition model with Transformer encoders that can be used in a streaming speech recognition system. Transformer computation blocks…
Toward domain-invariant speech recognition via large scale training
Arun Narayanan, Ananya Misra, Khe Chai Sim +6
Current state-of-the-art automatic speech recognition systems are trained to work in specific `domains', defined based on factors like application, sampling rate and codec. When su…
Speech recognition for medical conversations
Chung-Cheng Chiu, Anshuman Tripathi, Katherine Chou +11
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ($1…