Abstract
In this work, we present the performance evaluation of an implicit approach for the automatic segmentation of continuous speech signals into broad phonemic classes as encountered in Greek language. Our framework was evaluated with clear speech and speech with white, pink, bubble, car and machine gun additive noise. Our framework’s results were very promising since an accuracy of 76.1% was achieved for the case of clear speech (for distances less than 25 msec to the actual segmentation point), without presenting over-segmentation on the speech signal. An average reduction of 4% in the total accuracy of our segmentation framework was observed in the case of wideband distortion additive noise environment.
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© 2006 Springer-Verlag Berlin Heidelberg
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Mporas, I., Zervas, P., Fakotakis, N. (2006). Detecting Broad Phonemic Class Boundaries from Greek Speech in Noise Environments. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2006. Lecture Notes in Computer Science(), vol 4188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11846406_60
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DOI: https://doi.org/10.1007/11846406_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39090-9
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