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Articulatory and excitation source features for speech recognition in read, extempore and conversation modes

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Abstract

In our previous works, we have explored articulatory and excitation source features to improve the performance of phone recognition systems (PRSs) using read speech corpora. In this work, we have extended the use of articulatory and excitation source features for developing PRSs of extempore and conversation modes of speech, in addition to the read speech. It is well known that the overall performance of speech recognition system heavily depends on accuracy of phone recognition. Therefore, the objective of this paper is to enhance the accuracy of phone recognition systems using articulatory and excitation source features in addition to conventional spectral features. The articulatory features (AFs) are derived from the spectral features using feedforward neural networks (FFNNs). We have considered five AF groups, namely: manner, place, roundness, frontness and height. Five different AF-based tandem PRSs are developed using the combination of Mel frequency cepstral coefficients (MFCCs) and AFs derived from FFNNs. Hybrid PRSs are developed by combining the evidences from AF-based tandem PRSs using weighted combination approach. The excitation source information is derived by processing the linear prediction residual of the speech signal. The vocal tract information is captured using MFCCs. The combination of vocal tract and excitation source features is used for developing PRSs. The PRSs are developed using hidden Markov models. Bengali speech database is used for developing PRSs of read, extempore and conversation modes of speech. The results are analyzed and the performance is compared across different modes of speech. From the results, it is observed that the use of either articulatory or excitation source features along-with to MFCCs will improve the performance of PRSs in all three modes of speech. The improvement in the performance using AFs is much higher compared to the improvement obtained using excitation source features.

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Acknowledgments

The work presented in this paper was performed at IIT-Kharagpur as a part of the project 11(6)/2011-HCC(TDIL) , Dt. 23-12-2011, “Prosodically guided phonetic engine for searching speech databases in Indian languages” supported by Department of Information Technology, Government of India.

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  1. School of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    K. E. Manjunath & K. Sreenivasa Rao

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  1. K. E. Manjunath
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  2. K. Sreenivasa Rao
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Correspondence to K. Sreenivasa Rao.

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Manjunath, K.E., Sreenivasa Rao, K. Articulatory and excitation source features for speech recognition in read, extempore and conversation modes. Int J Speech Technol 19, 121–134 (2016). https://doi.org/10.1007/s10772-015-9329-x

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  • DOI: https://doi.org/10.1007/s10772-015-9329-x

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