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Recognition of Tones in YorÙbÁ Speech: Experiments With Artificial Neural Networks

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Speech, Audio, Image and Biomedical Signal Processing using Neural Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 83))

The speech recognition technology has been applied with success to many Western and Asian languages. Work on African languages still remains very limited in this area. Here a study into automatic recognition of the Standard Yoruba (SY) language tones is described. The models used fundamental frequency profile of SY syllables to characterize and discriminate the three Yoruba tones. Tonal parameters were selected carefully based on linguistic knowledge of tones and observation of acoustic data. We experimented with Multi-layered Perceptron (MLP) and Recurrent Neural Network (RNN) models by training them to classify feature parameters corresponding to tonal patterns. The results obtained exhibited good performances for the two tone recognition models, although the RNN achieved accuracy rates which are higher than that of the MLP model. For example, the outside tests for the H tone, produced a recognition accuracy of 71.00 and 76.00% for the MLP and the RNN models, respectively. In conclusion, this study has demonstrated a basic approach to tone recognition for Yoruba using Artificial Neural Networks (ANN). The proposed model can be easily extended to other African tone languages.

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  1. Ọbáfémi Awólòwò University, Ilé-Ifè, Nigeria

    Ọdétúnjí Àjàdí ỌDélọBí

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  1. Ọdétúnjí Àjàdí ỌDélọBí
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  1. Department of Computer and Information Sciences, Florida A&M University, Tallahassee, FL 32307, USA

    Bhanu Prasad

  2. Department of Electronics and Communication Engineering, Indian Institute of Technology Guwahati, Guwahati, India

    S. R. Mahadeva Prasanna

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ỌDélọBí, Ọ.À. (2008). Recognition of Tones in YorÙbÁ Speech: Experiments With Artificial Neural Networks. In: Prasad, B., Prasanna, S.R.M. (eds) Speech, Audio, Image and Biomedical Signal Processing using Neural Networks. Studies in Computational Intelligence, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75398-8_2

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  • DOI: https://doi.org/10.1007/978-3-540-75398-8_2

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