Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM)
Most state-of-the-art large vocabulary continuous speech recognition systems employ context dependent (CD) phone units, however, the CD phone units are not efficient in capturing long-term spectral dependencies of tone in most tone languages. The Standard Yorùbá (SY) is a language composed of sylla...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)
2019-06-01
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| Series: | Journal of Applied Sciences and Environmental Management |
| Subjects: | |
| Online Access: | https://www.ajol.info/index.php/jasem/article/view/187585 |
| Summary: | Most state-of-the-art large vocabulary continuous speech recognition systems employ context dependent (CD) phone units, however, the CD phone units are not efficient in capturing long-term spectral dependencies of tone in most tone languages. The Standard Yorùbá (SY) is a language composed of syllable with tones and requires different method for the acoustic modeling. In this paper, a context dependent tone acoustic model was developed. Tone unit is assumed as syllables, amplitude magnified difference function (AMDF) was used to derive the utterance wide F contour, followed by automatic syllabification and tri-syllable forced alignment with speech phonetization alignment and syllabification SPPAS tool. For classification of the context dependent (CD) tone, slope and intercept of F values were extracted from each segmented unit. Supervised clustering scheme was utilized to partition CD tri-tone based on category and normalized based on some statistics to derive the acoustic feature vectors. Multi-class support vector machine (MSVM) was used for tri-tone training. From the experimental results, it was observed that the word recognition accuracy obtained from the MSVM tri-tone system based on dynamic programming tone embedded features was comparable with phone features. A best parameter tuning was obtained for 10-fold cross validation and overall accuracy was 97.5678%. In term of word error rate (WER), the MSVM CD tri-tone system outperforms the hidden Markov model tri-phone system with WER of 44.47%.
Keywords: Syllabification, Standard Yorùbá, Context Dependent Tone, Tri-tone Recognition
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| ISSN: | 2659-1502 2659-1499 |