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arXiv · 2008.02897

Iterative Compression of End-to-End ASR Model using AutoML

Abhinav Mehrotra·Łukasz Dudziak·Jinsu Yeo·Young-yoon Lee·Ravichander Vipperla·Mohamed S. Abdelfattah·Sourav Bhattacharya·Samin Ishtiaq·Alberto Gil C. P. Ramos·SangJeong Lee·Daehyun Kim·Nicholas D. Lane
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Abstract

Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research have shown that AutoML-based Low Rank Factorization (LRF) technique, when applied to an end-to-end Encoder-Attention-Decoder style ASR model, can achieve a speedup of up to 3.7x, outperforming laborious manual rank-selection approaches. However, we show that current AutoML-based search techniques only work up to a certain compression level, beyond which they fail to produce compressed models with acceptable word error rates (WER). In this work, we propose an iterative AutoML-based LRF approach that achieves over 5x compression without degrading the WER, thereby advancing the state-of-the-art in ASR compression.

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Abhinav Mehrotra, Łukasz Dudziak, Jinsu Yeo, Young-yoon Lee, Ravichander Vipperla, Mohamed S. Abdelfattah, Sourav Bhattacharya, Samin Ishtiaq, Alberto Gil C. P. Ramos, SangJeong Lee, Daehyun Kim, Nicholas D. Lane. 2020-08-06. Iterative Compression of End-to-End ASR Model using AutoML. https://arxiv.org/abs/2008.02897

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