Hidden Markov Model-based approach for nasalized vowels recognition in spontaneous speech
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چکیده
In this study, oral and nasalized vowels were analysed based on Hungarian spontaneous speech corpus. Although such results are generally based on analyses of isolated, read-aloud sentences, the authors suggested it is questionable that they are also true of spontaneous types of speech. There is a lack of agreement in the literature as to which measurable acoustic parameters correlate of nasality. MFCC as robust feature was presented earlier for nasalized vowel detection combined with SVM classifier. In this research, we investigated the use MFCC and HMM for automatic nasalized vowel recognition. Results support the view i) regressive nasalization could be classified with better accuracy than progressive nasalization, ii) the degree of nasalization strongly depends on the vowel quality, iii) low vowels show a large degree of articulatory nasalization, however, the acoustic consequences are smaller, therefore the perceived degree of nasalization is either similar or lesser than for higher vowels.
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تاریخ انتشار 2015