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Automatic syllabification of speech signal using short time energy and vowel onset points

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Abstract

This paper describes a language independent method for automatic syllabification of speech signal. This method utilizes the valleys in short time energy (STE) contour and location of vowel onset points (VOP) for marking the syllable boundaries. In the proposed method, automatic syllabification is performed in three steps. First, long silence/pause regions are marked with the help of speech/non-speech detection. Then VOPs are located from the Hilbert Envelope of LP residual. The existence of more than one VOP in a continuous speech region (identified using speech/non-speech detection in the first step) is an indication of syllable boundaries within the region. Location with minimum energy in the STE contour between two consecutive VOP is identified as the syllable boundary. Since automatic VOP detection algorithm fails to detect some of the VOPs, certain syllable boundaries will be missed. Therefore, at the third step, additional syllable boundaries are detected from STE contour by fixing a valley threshold which is equal to the mean value of STE corresponding to each speech region between two consecutive syllable boundaries. This method is evaluated for 50 sentences each in read, extempore and conversational mode speech of Malayalam and Bengali languages. Overall accuracy of 80% is obtained with ± 50 ms tolerance with reference to manually marked syllable boundaries for this database. Method also shows good accuracy in case of TIMIT and NTIMIT data without tuning of thresholds and other parameters. This method is useful for applications that do not require exact syllable boundaries, rather a meaningful separation of syllables. Application of this technique for prosody based emotion recognition is illustrated using Emo-DB German emotional database.

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Acknowledgements

The authors would like to thank Kerala State Council for Science, Technology and Environment (KSCSTE), Government of Kerala, India for their support.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Government Engineering College, Idukki, Kerala, India

    Leena Mary

  2. Department of Electronics and Communication Engineering, Rajiv Gandhi Institute of Technology, Kottayam, Kerala, India

    Anil P. Antony & Ben P. Babu

  3. Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, India

    S. R. Mahadeva Prasanna

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  1. Leena Mary
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  2. Anil P. Antony
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  3. Ben P. Babu
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  4. S. R. Mahadeva Prasanna
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Correspondence to Leena Mary.

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Mary, L., Antony, A.P., Babu, B.P. et al. Automatic syllabification of speech signal using short time energy and vowel onset points. Int J Speech Technol 21, 571–579 (2018). https://doi.org/10.1007/s10772-018-9517-6

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  • DOI: https://doi.org/10.1007/s10772-018-9517-6

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