Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features

TL;DR

This paper introduces an unsupervised gender-based domain adaptation method for speech recognition that leverages phonetic features to improve accuracy across different speaker genders and domain variations.

Contribution

It proposes a novel unsupervised domain adaptation technique using phonetic features specifically for gender-based variability in speech recognition.

Findings

Significant reduction in phoneme error rate on TIMIT dataset

Effective adaptation across gender and domain variations

Demonstrates the utility of phonetic features in domain adaptation

Abstract

Automatic speech recognition is a difficult problem in pattern recognition because several sources of variability exist in the speech input like the channel variations, the input might be clean or noisy, the speakers may have different accent and variations in the gender, etc. As a result, domain adaptation is important in speech recognition where we train the model for a particular source domain and test it on a different target domain. In this paper, we propose a technique to perform unsupervised gender-based domain adaptation in speech recognition using phonetic features. The experiments are performed on the TIMIT dataset and there is a considerable decrease in the phoneme error rate using the proposed approach.