Language Model Speaker Adaptation for Transcription of Slovak Parliament Proceedings

Abstract

Language model and acoustic model adaptation play an important role in enhancing performance and robustness of automatic speech recognition, especially in the case of domain-specific, gender-dependent, or user-adapted systems development. This paper is oriented on the language model speaker adaptation for transcription of parliament proceedings in Slovak for individual speaker. Based on the current research studies, we have developed a framework combining multiple speech recognition outputs with acoustic and language model adaptation at different stages. The preliminary results show a significant decrease in the model perplexity from 45 % to 74 % relatively and the speech recognition word error rate from 29 % to 43 %, for male and female speakers respectively.