A method based on ensemble empirical mode decomposition (EEMD) is proposed for accurately detecting the time varying pitch of speech in tonal languages. Unlike frame-, event-, or subspace-based pitch detectors, the ti...A method based on ensemble empirical mode decomposition (EEMD) is proposed for accurately detecting the time varying pitch of speech in tonal languages. Unlike frame-, event-, or subspace-based pitch detectors, the time varying information of pitch within the short duration, which is of crucial importance in speech processing of tonal languages, can be accurately extracted. The Chinese Linguistic Data Consortium (CLDC) database for Mandarin Chinese was employed as standard speech data for the evaluation of the effectiveness of the method. It is shown that the proposed method provides more accurate and reliable results, particularly in estimating the tones of non-monotonically varying pitches like the third one in Mandarin Chinese. Also, it is shown that the new method has strong resistance to noise disturbance.展开更多
基金supported by the National Natural Science Foundation of China (No. 10574070)the State Key Laboratory Foundation of China (No. 9140C240207060C24)
文摘A method based on ensemble empirical mode decomposition (EEMD) is proposed for accurately detecting the time varying pitch of speech in tonal languages. Unlike frame-, event-, or subspace-based pitch detectors, the time varying information of pitch within the short duration, which is of crucial importance in speech processing of tonal languages, can be accurately extracted. The Chinese Linguistic Data Consortium (CLDC) database for Mandarin Chinese was employed as standard speech data for the evaluation of the effectiveness of the method. It is shown that the proposed method provides more accurate and reliable results, particularly in estimating the tones of non-monotonically varying pitches like the third one in Mandarin Chinese. Also, it is shown that the new method has strong resistance to noise disturbance.
