Domain-Aware Neural Language Models for Speech Recognition
Linda Liu, Yile Gu, Aditya Gourav, Ankur Gandhe, Shashank Kalmane, Denis Filimonov, Ariya Rastrow, Ivan Bulyko
Abstract
As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-aware rescoring framework suitable for achieving domain-adaptation during second-pass rescoring in production settings. In our framework, we fine-tune a domain-general neural language model on several domains, and use an LSTM-based domain classification model to select the appropriate domain-adapted model to use for second-pass rescoring. This domain-aware rescoring improves the word error rate by up to 2.4% and slot word error rate by up to 4.1% on three individual domains – shopping, navigation, and music – compared to domain general rescoring. These improvements are obtained while maintaining accuracy for the general use case.
BibTeX
@inproceedings{icassp2021_domainawareneura,
title = {Domain-Aware Neural Language Models for Speech Recognition},
author = {Linda Liu and Yile Gu and Aditya Gourav and Ankur Gandhe and Shashank Kalmane and Denis Filimonov and Ariya Rastrow and Ivan Bulyko},
booktitle = {ICASSP 2021},
year = {2021}
}