26 citations · 29 across the 5 of their papers we have counts for
5 papers
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation
Stefan Braun, Erik McDermott, Roger Hsiao
The neural transducer is an end-to-end model for automatic speech recognition (ASR). While the model is well-suited for streaming ASR, the training process remains challenging. Dur…
Variable Attention Masking for Configurable Transformer Transducer Speech Recognition
Pawel Swietojanski, Stefan Braun, Dogan Can +9
This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios. We pr…
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation
Thien Nguyen, Nathalie Tran, Liuhui Deng +16
Code-switching describes the practice of using more than one language in the same sentence. In this study, we investigate how to optimize a neural transducer based bilingual automa…
A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognition
Erik McDermott, Hasim Sak, Ehsan Variani
This article describes a density ratio approach to integrating external Language Models (LMs) into end-to-end models for Automatic Speech Recognition (ASR). Applied to a Recurrent…
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…