Lehabik, D., Merzban, M., Saad, S., Gody, A. (2020). Broad Phonetic Classification of ASR using Visual Based Features. The Egyptian Journal of Language Engineering, 7(1), 14-26. doi: 10.21608/ejle.2020.24358.1003
Doaa Ahmed Lehabik; Mohamed H. Merzban; Sameh F. Saad; Amr M. Gody. "Broad Phonetic Classification of ASR using Visual Based Features". The Egyptian Journal of Language Engineering, 7, 1, 2020, 14-26. doi: 10.21608/ejle.2020.24358.1003
Lehabik, D., Merzban, M., Saad, S., Gody, A. (2020). 'Broad Phonetic Classification of ASR using Visual Based Features', The Egyptian Journal of Language Engineering, 7(1), pp. 14-26. doi: 10.21608/ejle.2020.24358.1003
Lehabik, D., Merzban, M., Saad, S., Gody, A. Broad Phonetic Classification of ASR using Visual Based Features. The Egyptian Journal of Language Engineering, 2020; 7(1): 14-26. doi: 10.21608/ejle.2020.24358.1003
Broad Phonetic Classification of ASR using Visual Based Features
1Department of Communication and Electronic
Faculty of Engineering
Fayoum University
2Faculty of Engineering Fayoum University
3Faculty of engineering
4Faculty of Engineering, Fayoum University
Abstract
Abstract: This paper presents a novel method of classifying speech phonemes. Four hybrid techniques based on the acoustic-phonetic approach and pattern recognition approach are used to emphasize the principle idea of this research. The first hybrid model is constructed of fixed state, structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network, Vector Quantization (FS-HMM-GM-MBTI-CNN-VQ). The second hybrid model is constructed of variable state, dynamically structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network, Vector Quantization (VS-HMM-GM-MBTI-CNN-VQ). The third hybrid model is constructed of fixed state, structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network (FS-HMM-GM-MBTI-CNN). The fourth hybrid model is constructed of variable state, dynamically structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network (VS-HMM-GM-MBTI-CNN). TIMIT database is used in this paper. All phones are classified into five classes and segregated into Vowels, Plosives, Fricatives, Nasals, and Silences. The results show that using (VS-HMM-GM-MBTI-CNN-VQ) is an available method for classification of phonemes, with the potential for use in applications such as automatic speech recognition and automatic language identification. Competitive results are achieved especially in nasals, plosives, and silence high successive rates than others.
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