Rescoring N-Best Hypotheses for Arabic Speech Recognition: A Syntax-Mining Approach

Improving speech recognition accuracy through linguistic knowledge is a major research area in automatic speech recognition systems. In this paper, we present a syntax-mining approach to rescore N-Best hypotheses for Arabic speech recognition systems. The method depends on a machine learning tool (WEKA-3-6-5) to extract the N-Best syntactic rules of the Baseline tagged transcription corpus which was tagged using Stanford Arabic tagger. The proposed method was tested using the Baseline system that contains a pronunciation dictionary of 17,236 vocabularies (28,682 words and variants) from 7.57 hours pronunciation corpus of modern standard Arabic (MSA) broadcast news. Using Carnegie Mellon University (CMU) PocketSphinx speech recognition engine, the Baseline system achieved a Word Error Rate (WER) of 16.04 % on a test set of 400 utterances (about 0.57 hours) containing 3585 diacritized words. Even though there were enhancements in some tested files, we found that this method does not lead to significant enhancement (for Arabic). Based on this research work, we conclude this paper by introducing a new design for language models to account for longer-distance constrains, instead of a few proceeding words.