1 research outputs found
Alignment Restricted Streaming Recurrent Neural Network Transducer
There is a growing interest in the speech community in developing Recurrent
Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR)
applications. RNN-T is trained with a loss function that does not enforce
temporal alignment of the training transcripts and audio. As a result, RNN-T
models built with uni-directional long short term memory (LSTM) encoders tend
to wait for longer spans of input audio, before streaming already decoded ASR
tokens. In this work, we propose a modification to the RNN-T loss function and
develop Alignment Restricted RNN-T (Ar-RNN-T) models, which utilize audio-text
alignment information to guide the loss computation. We compare the proposed
method with existing works, such as monotonic RNN-T, on LibriSpeech and
in-house datasets. We show that the Ar-RNN-T loss provides a refined control to
navigate the trade-offs between the token emission delays and the Word Error
Rate (WER). The Ar-RNN-T models also improve downstream applications such as
the ASR End-pointing by guaranteeing token emissions within any given range of
latency. Moreover, the Ar-RNN-T loss allows for bigger batch sizes and 4 times
higher throughput for our LSTM model architecture, enabling faster training and
convergence on GPUs.Comment: Accepted for presentation at IEEE Spoken Language Technology Workshop
(SLT) 202