In this paper, a new event detection pitch detector based on the dyadic wavelet transform was constrcted by selecting an optimal scale. The proposed pitch detector is accurate, robust to noise and computationally simp...In this paper, a new event detection pitch detector based on the dyadic wavelet transform was constrcted by selecting an optimal scale. The proposed pitch detector is accurate, robust to noise and computationally simple. Experiments show the superior performance of this event-based pitch detector in comparison with previous event-based pitch detector and classical pitch detectors that use the autocorrelation and the cepsmun methods to estimate the pitch period.展开更多
This paper proposes an algorithm that adopts the harmonic regeneration as post-processing to improve the performance of speech enhancement using traditional Short Time Spectral Amplitude(STSA).The proposed algorithm a...This paper proposes an algorithm that adopts the harmonic regeneration as post-processing to improve the performance of speech enhancement using traditional Short Time Spectral Amplitude(STSA).The proposed algorithm aims to alleviate the distortion of the high harmonics of enhanced speech via the traditional STSA,and consequently improves the speech quality.We first detect the pitch,or fundamental frequency,of the enhanced speech via the traditional STSA,and then,divide the whole spectrum into multiple sub-bands which center on each harmonic.After that,a series of specially designed windows centered on each harmonic are applied to all the sub-bands,in order to redistribute the energy in the sub-bands.The results of experiment demonstrate that the method has both theo-retical and practical basis.展开更多
Articulatory features describe how articulators are involved in making sounds.Speakers often use a more exaggerated way to pronounce accented phonemes,so articulatory features can be helpful in pitch accent detection....Articulatory features describe how articulators are involved in making sounds.Speakers often use a more exaggerated way to pronounce accented phonemes,so articulatory features can be helpful in pitch accent detection.Instead of using the actual articulatory features obtained by direct measurement of articulators,we use the posterior probabilities produced by multi-layer perceptrons(MLPs) as articulatory features.The inputs of MLPs are frame-level acoustic features pre-processed using the split temporal context-2(STC-2) approach.The outputs are the posterior probabilities of a set of articulatory attributes.These posterior probabilities are averaged piecewise within the range of syllables and eventually act as syllable-level articulatory features.This work is the first to introduce articulatory features into pitch accent detection.Using the articulatory features extracted in this way,together with other traditional acoustic features,can improve the accuracy of pitch accent detection by about 2%.展开更多
This paper presents a reliable speaker-independent method of recognizing Chinese tones. An unbiased center-clipping autocorrelation algorithm of pitch period extraction is proposed. A two-dimensional decision vector i...This paper presents a reliable speaker-independent method of recognizing Chinese tones. An unbiased center-clipping autocorrelation algorithm of pitch period extraction is proposed. A two-dimensional decision vector is used for recognizing Chinese tones by passing the pitch period sequence through the procedures of data selection, error correction, data smoothing and curve fitting. The average correct rate of tone recognition for isolated Chinese syllables is over 98%.展开更多
文摘In this paper, a new event detection pitch detector based on the dyadic wavelet transform was constrcted by selecting an optimal scale. The proposed pitch detector is accurate, robust to noise and computationally simple. Experiments show the superior performance of this event-based pitch detector in comparison with previous event-based pitch detector and classical pitch detectors that use the autocorrelation and the cepsmun methods to estimate the pitch period.
基金Supported by the National Natural Science Foundation of China (No. 60572081)
文摘This paper proposes an algorithm that adopts the harmonic regeneration as post-processing to improve the performance of speech enhancement using traditional Short Time Spectral Amplitude(STSA).The proposed algorithm aims to alleviate the distortion of the high harmonics of enhanced speech via the traditional STSA,and consequently improves the speech quality.We first detect the pitch,or fundamental frequency,of the enhanced speech via the traditional STSA,and then,divide the whole spectrum into multiple sub-bands which center on each harmonic.After that,a series of specially designed windows centered on each harmonic are applied to all the sub-bands,in order to redistribute the energy in the sub-bands.The results of experiment demonstrate that the method has both theo-retical and practical basis.
基金Project(Nos.61370034,61273268,and 61005019) supported by the National Natural Science Foundation of China
文摘Articulatory features describe how articulators are involved in making sounds.Speakers often use a more exaggerated way to pronounce accented phonemes,so articulatory features can be helpful in pitch accent detection.Instead of using the actual articulatory features obtained by direct measurement of articulators,we use the posterior probabilities produced by multi-layer perceptrons(MLPs) as articulatory features.The inputs of MLPs are frame-level acoustic features pre-processed using the split temporal context-2(STC-2) approach.The outputs are the posterior probabilities of a set of articulatory attributes.These posterior probabilities are averaged piecewise within the range of syllables and eventually act as syllable-level articulatory features.This work is the first to introduce articulatory features into pitch accent detection.Using the articulatory features extracted in this way,together with other traditional acoustic features,can improve the accuracy of pitch accent detection by about 2%.
基金The Project is Supported by the National Natural Science Foundation of China
文摘This paper presents a reliable speaker-independent method of recognizing Chinese tones. An unbiased center-clipping autocorrelation algorithm of pitch period extraction is proposed. A two-dimensional decision vector is used for recognizing Chinese tones by passing the pitch period sequence through the procedures of data selection, error correction, data smoothing and curve fitting. The average correct rate of tone recognition for isolated Chinese syllables is over 98%.
