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: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)
2019-06-01
|
| Series: | Journal of Applied Sciences and Environmental Management |
| Subjects: | |
| Online Access: | https://www.ajol.info/index.php/jasem/article/view/187585 |
| _version_ | 1797228006443319296 |
|---|---|
| author | A.A. Sosimi T. Adegbola O.A. Fakinlede |
| author_facet | A.A. Sosimi T. Adegbola O.A. Fakinlede |
| author_sort | A.A. Sosimi |
| collection | DOAJ |
| description |
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
|
| first_indexed | 2024-04-24T14:49:50Z |
| format | Article |
| id | doaj.art-0087a4bda22d49149e5eb1adbe003818 |
| institution | Directory Open Access Journal |
| issn | 2659-1502 2659-1499 |
| language | English |
| last_indexed | 2024-04-24T14:49:50Z |
| publishDate | 2019-06-01 |
| publisher | Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP) |
| record_format | Article |
| series | Journal of Applied Sciences and Environmental Management |
| spelling | doaj.art-0087a4bda22d49149e5eb1adbe0038182024-04-02T19:50:38ZengJoint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)Journal of Applied Sciences and Environmental Management2659-15022659-14992019-06-0123510.4314/jasem.v23i5.20Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM)A.A. SosimiT. AdegbolaO.A. Fakinlede 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 https://www.ajol.info/index.php/jasem/article/view/187585SyllabificationStandard YorùbáContext Dependent ToneTri-tone Recognition |
| spellingShingle | A.A. Sosimi T. Adegbola O.A. Fakinlede Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM) Journal of Applied Sciences and Environmental Management Syllabification Standard Yorùbá Context Dependent Tone Tri-tone Recognition |
| title | Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM) |
| title_full | Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM) |
| title_fullStr | Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM) |
| title_full_unstemmed | Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM) |
| title_short | Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM) |
| title_sort | standard yoruba context dependent tone identification using multi class support vector machine msvm |
| topic | Syllabification Standard Yorùbá Context Dependent Tone Tri-tone Recognition |
| url | https://www.ajol.info/index.php/jasem/article/view/187585 |
| work_keys_str_mv | AT aasosimi standardyorubacontextdependenttoneidentificationusingmulticlasssupportvectormachinemsvm AT tadegbola standardyorubacontextdependenttoneidentificationusingmulticlasssupportvectormachinemsvm AT oafakinlede standardyorubacontextdependenttoneidentificationusingmulticlasssupportvectormachinemsvm |