Improved Arabic speech recognition system through the automatic generation of fine-grained phonetic transcriptions

This paper aims at determining the best way to exploit the phonological properties of the Arabic language in order to improve the performance of the speech recognition system. One of the main challenges facing the processing of Arabic is the effect of the local context, which induces changes in the phonetic representation of a given text, thereby causing the recognition engine to misclassify it. The proposed solution is to develop a set of language-dependent grapheme-to-allophone rules that can predict such allophonic variations and hence provide a phonetic transcription that is sensitive to the local context for the automatic speech recognition system. The novel aspect of this method is that the pronunciation of each word is extracted directly from a context-sensitive phonetic transcription rather than a predefined dictionary that typically does not reflect the actual pronunciation of the word. The paper also aims at employing the stress feature as one of the supra-segmental characteristics of speech to enhance the acoustic modelling. The effectiveness of applying the proposed rules has been tested by comparing the performance of a dictionary based system against one using the automatically generated phonetic transcription. The research reported an average of 9.3% improvement in the system's performance by eliminating the fixed dictionary and using the generated phonetic transcription to learn the phone probabilities. Marking the stressed vowels with separate stress markers leads to a further improvement of 1.7%. (C) 2017 Elsevier Ltd. All rights reserved.