Evaluation of speech unit modelling for HMM-based speech synthesis for Arabic

Abstract

This paper investigates the use of hidden Markov models (HMM) for Modern Standard Arabic speech synthesis. HMM-based speech synthesis systems require a description of each speech unit with a set of contextual features that specifies phonetic, phonological and linguistic aspects. To apply this method to Arabic language, a study of its particularities was conducted to extract suitable contextual features. Two phenomena are highlighted: vowel quantity and gemination. This work focuses on how to model geminated consonants (resp. long vowels), either considering them as fully-fledged phonemes or as the same phonemes as their simple (resp. short) counterparts but with a different duration. Four modelling approaches have been proposed for this purpose. Results of subjective and objective evaluations show that there is no important difference between differentiating modelling units associated to geminated consonants (resp. long vowels) from modelling units associated to simple consonants (resp. short vowels) and merging them as long as gemination and vowel quantity information is included in the set of features.