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Hybrid continuous speech recognition systems by HMM, MLP and SVM: a comparative study

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Abstract

This paper presents a new hybrid method for continuous Arabic speech recognition based on triphones modelling. To do this, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities within the Hidden Markov Models (HMM) standards. In this work, we describe a new approach of categorising Arabic vowels to long and short vowels to be applied on the labeling phase of speech signals. Using this new labeling method, we deduce that SVM/HMM hybrid model is more efficient then HMMs standards and the hybrid system Multi-Layer Perceptron (MLP) with HMM. The obtained results for the Arabic speech recognition system based on triphones are 64.68 % with HMMs, 72.39 % with MLP/HMM and 74.01 % for SVM/HMM hybrid model. The WER obtained for the recognition of continuous speech by the three systems proves the performance of SVM/HMM by obtaining the lowest average for 4 tested speakers 11.42 %.

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Authors and Affiliations

  1. National School of Engineers of Sfax, El Habib City, Sfax, Tunisia

    Elyes Zarrouk & Yassine Ben Ayed

  2. Higher Institute of Computer Science and Multimedia at Sfax University, Sfax University, Sfax, Tunisia

    Faiez Gargouri

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  1. Elyes Zarrouk
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  2. Yassine Ben Ayed
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  3. Faiez Gargouri
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Correspondence to Elyes Zarrouk.

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Zarrouk, E., Ben Ayed, Y. & Gargouri, F. Hybrid continuous speech recognition systems by HMM, MLP and SVM: a comparative study. Int J Speech Technol 17, 223–233 (2014). https://doi.org/10.1007/s10772-013-9221-5

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  • DOI: https://doi.org/10.1007/s10772-013-9221-5

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