Assistive speech-enabled systems are proposed to help both French and English speaking persons with various speech disorders. The proposed assistive systems use automatic speech recognition (ASR) and speech synthesis in order to enhance the quality of communication. These systems aim at improving the intelligibility of pathologic speech making it as natural as possible and close to the original voice of the speaker. The resynthesized utterances use new basic units, a new concatenating algorithm and a grafting technique to correct the poorly pronounced phonemes. The ASR responses are uttered by the new speech synthesis system in order to convey an intelligible message to listeners. Experiments involving four American speakers with severe dysarthria and two Acadian French speakers with sound substitution disorders (SSDs) are carried out to demonstrate the efficiency of the proposed methods. An improvement of the Perceptual Evaluation of the Speech Quality (PESQ) value of 5% and more than 20% is achieved by the speech synthesis systems that deal with SSD and dysarthria, respectively.

Emotional speech recognition for the Arabic language is insufficiently tackled in the literature compared to other languages. In this paper, we present the work of creating and verifying the King Saud University Emotions (KSUEmotions) corpus, which was released by the Linguistic Data Consortium (LDC) in 2017 as the first public Arabic emotional speech corpus. KSUEmotions contains an emotional speech of twenty-three speakers from Saudi Arabia, Syria, and Yemen, and includes the emotions: neutral, happiness, sadness, surprise, and anger. The corpus content is verified in two different ways: a human perceptual test by nine listeners who rate emotional performance in audio files, and automatic emotion recognition. Two automatic emotion recognition systems are experimented with: Residual Neural Network and Convolutional Neural Network. This work also experiments with emotion recognition for the English language using the Emotional Prosody Speech and Transcripts Corpus (EPST). The current experimental work is conducted in three tracks: (i) monolingual, where independent experiments for Arabic and English are carried out, (ii) multilingual, where the Arabic and English corpora are merged in as mixed corpus, and (iii) cross-lingual, where models are trained using one language and tested using the other. A challenge encountered in this work is that the two corpora do not contain the same emotions. That problem is tackled by mapping the emotions to the arousal-valance space.

Distinctive phonetic features have an important role in Arabic speech phoneme recognition. In a given language, distinctive phonetic features are extrapolated from acoustic features using different methods. However, exploiting lengthy acoustic features vector in the sake of phoneme recognition has a huge cost in terms of computational complexity, which in turn, affects real time applications. The aim of this work is to consider methods to reduce the size of features vector employed for distinctive phonetic feature and phoneme recognition. The objective is to select the relevant input features that contribute to the speech recognition process. This, in turn, will lead to a reduced computational complexity of recognition algorithm, and an improved recognition accuracy. In the proposed approach, genetic algorithm is used to perform optimal features selection. Therefore, a baseline model based on feedforward neural networks is first built. This model is used to benchmark the results of proposed features selection method with a method that employs all elements of a features vector. Experimental results, utilizing the King Abdulaziz City for Science and Technology Arabic Phonetic Database, show that the average genetic algorithm based phoneme overall recognition accuracy is maintained slightly higher than that of recognition method employing the full-fledge features vector. The genetic algorithm based distinctive phonetic features recognition method has achieved a 50% reduction in the dimension of the input vector while obtaining a recognition accuracy of 90%. Moreover, the results of the proposed method is validated using Wilcoxon signed rank test.