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EEMD-Based Speaker Automatic Emotional Recognition in Chinese Mandarin |
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PP: 617-624 |
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Author(s) |
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Yuqiang Qin,
Yudong Qi,
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Abstract |
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| Emotion feature extraction is the key to speech emotional recognition. And ensemble empirical mode
decomposition(EEMD) is a newly developed method aimed at eliminating emotion mode mixing present in the original empirical
mode decomposition(EMD). To evaluate the performance of this new method, this paper investigates the effect of a parameters
pertinent to EEMD: speech emotional envelope. Firstly, a speaker emotional envelope features extraction based on EEMD is proposed
in the paper. Using the piecewise power function in speech emotional envelope has a better effect in emotional identification. At the
same time, the proposed technique has been utilized for classification of four kinds of emotional(angry, happy, sad and neutral) speech
signals. Emotional intrinsic mode functions(IMFe) are obtained by empirical mode decomposition on emotional speech signals, the
fast fourier transform(FFT) of each intrinsic mode function is extracted as the emotional feature coefficient which is used in speaker
emotional identification applying by vector quantization. MATLAB is used to calculate the characteristic of emotional speech signals
using empirical mode decomposition (EEMD). We obtain an emotional envelope by transforming the IMFe of emotional speech
signals, and obtain a new method of emotion recognition according to different emotional envelop feature vectors. The results indicate
the proposed method works well in speaker emotional identification. |
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