Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features
This work addresses domain adaptation for speech recognition, specifically for gender variations, but appears incremental as it builds on existing phonetic feature methods.
The paper tackles the problem of domain adaptation in speech recognition by proposing an unsupervised technique using phonetic features for gender-based adaptation, resulting in a considerable decrease in phoneme error rate on the TIMIT dataset.
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.