Study of noise robustness of First Formant Bandwidth (F1BW) method
The performance of speech recognition application under adverse noisy condition often becomes the topic of researchers regardless of the language used. Applications that use vowel phonemes require high degree of Standard Malay vowel recognition capability.In Malaysia, researches in vowel recognitio...
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| 主要な著者: | , , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
2011
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| 主題: | |
| オンライン･アクセス: | http://repo.uum.edu.my/13624/1/99%20%281%29.pdf http://repo.uum.edu.my/13624/ http://www.icoci.cms.net.my |
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| 要約: | The performance of speech recognition application under adverse noisy condition often becomes the topic of researchers regardless of
the language used. Applications that use vowel phonemes require high degree of Standard Malay vowel recognition capability.In Malaysia, researches in vowel recognition is still lacking especially in the usage of Malay vowels, independent speaker systems, recognition robustness and algorithm speed and accuracy. This paper presents a noise robustness study
on an improved vowel feature extraction method called First Formant Bandwidth (F1BW) on three classifiers of Multinomial Logistic Regression
(MLR), K-Nearest Neighbors (k-NN) and Linear Discriminant Analysis (LDA).Results show that LDA performs best in overall vowel classification compared to MLR and KNN in terms of robustness capability. |
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