Duration modeling using DNN for Arabic speech synthesis

International audienceDuration modeling is a key task for every parametric speech synthesis system. Though such parametric systems have been adapted to many languages, no special attention was paid to explicitly handling Arabic speech characteristics. Actually, in Arabic phoneme duration has a distinctive role, because of consonant gemination and vowel quantity. Therefore, a precise modeling of sound durations is critical. In this paper we compare several modeling of phoneme durations (including duration modeling by HTS and MERLIN toolkits), and we propose a new approach which relies on using a set of models, each one being optimal for a given phoneme class (e.g., simple consonants, geminated consonants, short vowels, and long vowels). An objective evaluation carried out on a set of test sentences shows that the proposed approach leads to a more accurate modeling of the phoneme durations

Statistical modelling of speech units in HMM-based speech synthesis for Arabic

International audienceThis paper investigates statistical parametric speech synthesis of Modern Standard Arabic (MSA). Hidden Markov Models (HMM)-based speech synthesis system relies on a description of speech segments corresponding to phonemes, with a large set of features that represent phonetic, phonologic, linguistic and contextual aspects. When applied to MSA two specific phenomena have to be taken in account, the vowel lengthening and the consonant gemination. This paper studies thoroughly the modeling of these phenomena through various approaches: as for example, the use of different units for modeling short vs. long vowels and the use of different units for modeling simple vs. geminated consonants. These approaches are compared to another one which merges short and long variants of a vowel into a single unit and, simple and geminated variants of a consonant into a single unit (these characteristics being handled through the features associated to the sound). Results of subjective evaluation show that there is no significant difference between using the same unit for simple and geminated consonant (as well as for short and long vowels) and using different units for simple vs. geminated consonants (as well for short vs. long vowels)

F0 modeling using DNN for Arabic parametric speech synthesis

International audienceDeep neural networks (DNN) are gaining increasing interest in speech processing applications, especially in text-to-speech synthesis. Actually state-of-the-art speech generation tools, like MERLIN and WAVENET are totally DNN-based. However, every language has to be modeled on its own using DNN. One of the key components of speech synthesis modules is the prosodic parameters generation module from contextual input features, and more particularly the fundamental frequency (F0) generation module. Actually F0 is responsible for intonation , that is why it should be accurately modeled to provide intelligible and natural speech. However, F0 modeling is highly dependent on the language. Therefore, language specific characteristics have to be taken into account. In this paper, we aim to model F0 for Arabic speech synthesis with feedforward and recurrent DNN, and using specific characteristic features for Arabic like vowel quantity and gemination, in order to improve the quality of Arabic parametric speech synthesis

Duration modeling using DNN for Arabic speech synthesis

International audienceDuration modeling is a key task for every parametric speech synthesis system. Though such parametric systems have been adapted to many languages, no special attention was paid to explicitly handling Arabic speech characteristics. Actually, in Arabic phoneme duration has a distinctive role, because of consonant gemination and vowel quantity. Therefore, a precise modeling of sound durations is critical. In this paper we compare several modeling of phoneme durations (including duration modeling by HTS and MERLIN toolkits), and we propose a new approach which relies on using a set of models, each one being optimal for a given phoneme class (e.g., simple consonants, geminated consonants, short vowels, and long vowels). An objective evaluation carried out on a set of test sentences shows that the proposed approach leads to a more accurate modeling of the phoneme durations