Enhanced lengthening cancellation using bidirectional pitch similarity alignment for spontaneous speech.

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Bibliographic Details
Title: Enhanced lengthening cancellation using bidirectional pitch similarity alignment for spontaneous speech.
Authors: Shih, Po-Yi, Chen, Bo-Wei, Wang, Jhing-Fa, Wu, Jhing-Wei
Source: 2012 8th International Symposium on Chinese Spoken Language Processing; 1/ 1/2012, p238-242, 5p
Abstract: In this work, an enhanced lengthening cancellation method is proposed to detect and cancel the lengthening part of vowels. The proposed method consists of autocorrelation function, cosine similarity-based lengthening detection and bidirectional pitch contour alignment. Autocorrelation function is used to obtain the reference pitch contour. Cosine similarity-based method is applied to measure the similarity between the reference and the next adjacent pitch contours. Due to the variant lengths of periodic segments, fixed size frames may cause accumulative errors. Therefore, bidirectional pitch contour alignment is adopted in this study. Experiments indicate that the proposed method can achieve an accuracy rate of 91.4% and 88.7% on a 60-keyword and 50-scentence database, respectively. Moreover, the proposed approach performs about three times speed than the baseline. Such results prove the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]
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Database: Complementary Index
Description
Abstract:In this work, an enhanced lengthening cancellation method is proposed to detect and cancel the lengthening part of vowels. The proposed method consists of autocorrelation function, cosine similarity-based lengthening detection and bidirectional pitch contour alignment. Autocorrelation function is used to obtain the reference pitch contour. Cosine similarity-based method is applied to measure the similarity between the reference and the next adjacent pitch contours. Due to the variant lengths of periodic segments, fixed size frames may cause accumulative errors. Therefore, bidirectional pitch contour alignment is adopted in this study. Experiments indicate that the proposed method can achieve an accuracy rate of 91.4% and 88.7% on a 60-keyword and 50-scentence database, respectively. Moreover, the proposed approach performs about three times speed than the baseline. Such results prove the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]
ISBN:9781467325066
DOI:10.1109/ISCSLP.2012.6423517