Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM)
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Date
2019
Authors
Sosimi, A.A
Adegbola, T
Fakinlede, O.A
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Applied Sciences and Environmental Management
Abstract
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%.
Description
Keywords
Syllabification, Standard Yorùbá, Context Dependent Tone, Tri-tone Recognition
Citation
Sosimi, A. A., Adegbola, T., & Fakinlede, O. A. (2019). Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM). Journal of Applied Sciences and Environmental Management, 23(5), 895-901.