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Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization

2021-08-03 18:58:39
Jonathan Macoskey, Grant P. Strimel, Ariya Rastrow
arXiv_SD
                   
Abstract

We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks. The architecture enables a dynamic pivot for its runtime compute pathway, namely taking advantage of keyword spotting to select which component of the network to execute for a given audio frame. To accomplish this, we leverage a recurrent cell we call the Bifocal LSTM (BFLSTM), which we detail in the paper. The architecture is compatible with other optimization strategies such as quantization, sparsification, and applying time-reduction layers, making it especially applicable for deployed, real-time speech recognition settings. We present the architecture and report comparative experimental results on voice-assistant speech recognition tasks. Specifically, we show our proposed Bifocal RNN-T can improve inference cost by 29.1% with matching word error rates and only a minor increase in memory size.

Abstract (translated)

URL

https://arxiv.org/abs/2108.01704

PDF

https://arxiv.org/pdf/2108.01704.pdf

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