doi:

DOI: 10.3724/SP.J.1146.2006.01021

Journal of Electronics & Information Technology (电子与信息学报) 2008/30:2 PP.362-366

Endpoint Detection of Whispers Based on the Fitting Characteristic of EMD


Abstract:
Whispered speech is the especial form of people’s pronunciation. There is lower Signal-to-Noise Ratio (SNR) in whispers and unobvious pitch waveform compared with the normal speech, so it is more difficult to process the whispered speech. The endpoint detection of whispers is the first pivotal step of whispered speech signal processing. This paper uses the Empirical Mode Decomposition (EMD) of Hilbert-Huang Transform (HHT) to solve the problem, and firstly proposes a novel algorithm of endpoint detection of whispered speech based on the fitting characteristic of EMD. Normalize the energy of Intrinsic Mode Function (IMF) obtained by EMD, and use the fitting parameters of the energy as the characteristic and then the endpoint of whispers can be easily divided. The results of experiments show that it is very useful in endpoint detection of whispers, and the accurate rate is 98.25% in 1200 samples (SNR=2~10dB).

Key words:Hilbert-Huang Transform (HHT),Empirical Mode Decomposition (EMD),Intrinsic Mode Function (IMF),Fitting characteristic of normalized energy

ReleaseDate:2014-07-21 15:37:56



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