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arXiv · 2203.04767

A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling

Yike Zhang·Xiaobing Feng·Yi Liu·Songjun Cao·Long Ma
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Abstract

Automatic speech recognition (ASR) systems used on smart phones or vehicles are usually required to process speech queries from very different domains. In such situations, a vanilla ASR system usually fails to perform well on every domain. This paper proposes a multi-domain ASR framework for Tencent Map, a navigation app used on smart phones and in-vehicle infotainment systems. The proposed framework consists of three core parts: a basic ASR module to generate n-best lists of a speech query, a text classification module to determine which domain the speech query belongs to, and a reranking module to rescore n-best lists using domain-specific language models. In addition, an instance sampling based method to training neural network language models (NNLMs) is proposed to address the data imbalance problem in multi-domain ASR. In experiments, the proposed framework was evaluated on navigation domain and music domain, since navigating and playing music are two main features of Tencent Map. Compared to a general ASR system, the proposed framework achieves a relative 13% $\sim$ 22% character error rate reduction on several test sets collected from Tencent Map and our in-car voice assistant.

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Yike Zhang, Xiaobing Feng, Yi Liu, Songjun Cao, Long Ma. 2022-03-09. A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling. https://arxiv.org/abs/2203.04767

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