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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 4/2015

10.09.2015

Automatic prominent syllable detection with machine learning classifiers

verfasst von: David O. Johnson, Okim Kang

Erschienen in: International Journal of Speech Technology | Ausgabe 4/2015

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Abstract

In this paper, we examine the performance of automatically detecting Brazil’s prominent syllables using five machine learning classifiers and seven sets of features consisting of three features: pitch, intensity, and duration, taken one at time, two at a time, and all three. Prominent syllables are the foundation of Brazil’s prosodic intonation model. We found that using pitch, intensity, and duration as features produces the best optimal results. Our findings also revealed that in terms of accuracy, F-measure, and Cohen’s kappa coefficient that bagging an ensemble of decision tree learners performed the best (accuracy = 95.9 ± 0.2 %; F-measure = 93.7 ± 0.4; κ = 0.907 ± 0.005). The performance of our current model proves to be significantly better than any other automatic detection software that exists or that of human transcription experts of prosody.
Vorheriger Artikel Sensitivity of automatic speaker identification to SVD digital audio watermarking
Nächster Artikel Bayesian estimation for speech enhancement given a priori knowledge of clean speech phase

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Metadaten
Titel
Automatic prominent syllable detection with machine learning classifiers
verfasst von
David O. Johnson
Okim Kang
Publikationsdatum
10.09.2015
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 4/2015
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-015-9299-z

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