Emotion conversion for expressive Arabic text to speech

Speech processing applications have become integral components across various domains of modern life. The design and preparation of a reliable recognition system rely heavily on the availability of suitable speech databases. While numerous speech databases exist for English and other languages, the availability of comprehensive resources for Arabic language remains limited. In light of this, we conducted a systematic review aiming to identify, analyse, and classify existing Modern Standard Arabic speech databases. Through our review, we identified 27 publicly available databases and analysed an additional 80 subjective databases. These databases were thoroughly studied, classified based on their characteristics, and subjected to a detailed analysis of research trends in the field. This paper provides a comprehensive discussion on the diverse speech databases developed for various speech processing applications. It sheds light on the purposes and unique characteristics of Arabic speech databases, enabling researchers to easily access suitable resources for their specific applications. The findings of this review contribute to bridging the gap in available Arabic speech databases and serve as a valuable resource for researchers in the field.

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Modern Standard Arabic Speech Corpora: A Systematic Review

Alqadasi

Abdulghafor

Sunar

et al. 2023

IEEE Access

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Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model

2022

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The goal of emotional voice conversion (EVC) is to convert the emotion of a speaker’s voice from one state to another while maintaining the original speaker’s identity and the linguistic substance of the message. Research on EVC in the Arabic language is well behind that conducted on languages with a wider distribution, such as English. The primary objective of this study is to determine whether Arabic emotions may be converted using a model trained for another language. In this work, we used an unsupervised many-to-many non-parallel generative adversarial network (GAN) voice conversion (VC) model called StarGANv2-VC to perform an Arabic EVC (A-EVC). The latter is realized by using pre-trained phoneme-level automatic speech recognition (ASR) and fundamental frequency (F0) models in the English language. The generated voice is evaluated by prosody and spectrum conversion in addition to automatic emotion recognition and speaker identification using a convolutional recurrent neural network (CRNN). The results of the evaluation indicated that male voices were scored higher than female voices and that the evaluation score for the conversion from neutral to other emotions was higher than the evaluation scores for the conversion of other emotions.

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Emotion conversion for expressive Arabic text to speech

Cited by 2 publications

References 18 publications

Modern Standard Arabic Speech Corpora: A Systematic Review

Modern Standard Arabic Speech Corpora: A Systematic Review

Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model

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