Seed Words Based Data Selection for Language Model Adaptation

TL;DR

This paper introduces a method for selecting domain-specific sentences from a corpus to adapt language models for speech recognition systems, improving recognition of specialized terminology with limited data.

Contribution

It proposes novel data selection strategies based on morphological seeds and semantic similarity to enhance language model adaptation in ASR systems.

Findings

Reduced OOV rate in domain-specific ASR tasks

Improved WER for specialized terminology recognition

Effective selection of relevant sentences using proposed methods

Abstract

We address the problem of language model customization in applications where the ASR component needs to manage domain-specific terminology; although current state-of-the-art speech recognition technology provides excellent results for generic domains, the adaptation to specialized dictionaries or glossaries is still an open issue. In this work we present an approach for automatically selecting sentences, from a text corpus, that match, both semantically and morphologically, a glossary of terms (words or composite words) furnished by the user. The final goal is to rapidly adapt the language model of an hybrid ASR system with a limited amount of in-domain text data in order to successfully cope with the linguistic domain at hand; the vocabulary of the baseline model is expanded and tailored, reducing the resulting OOV rate. Data selection strategies based on shallow morphological seeds and semantic similarity viaword2vec are introduced and discussed; the experimental setting consists in a simultaneous interpreting scenario, where ASRs in three languages are designed to recognize the domain-specific terms (i.e. dentistry). Results using different metrics (OOV rate, WER, precision and recall) show the effectiveness of the proposed techniques.