Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization

TL;DR

This paper introduces Bifocal RNN-T, a novel speech recognition architecture that leverages keyword spotting to optimize inference latency, achieving significant cost reductions while maintaining accuracy.

Contribution

The paper proposes Bifocal RNN-T with Bifocal LSTM, enabling dynamic computation pathways based on keyword spotting for improved inference efficiency.

Findings

Achieves 29.1% reduction in inference cost

Maintains comparable word error rates

Compatible with quantization and sparsification techniques

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.