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Harimi, Ali, Shahzadi, Ali, Ahmadyfard, Alireza, Yaghmaie, Khashayar. (1393). Classification of emotional speech using spectral pattern features. , 2(1), 53-61. doi: 10.22044/jadm.2014.150
Ali Harimi; Ali Shahzadi; Alireza Ahmadyfard; Khashayar Yaghmaie. "Classification of emotional speech using spectral pattern features". , 2, 1, 1393, 53-61. doi: 10.22044/jadm.2014.150
Harimi, Ali, Shahzadi, Ali, Ahmadyfard, Alireza, Yaghmaie, Khashayar. (1393). 'Classification of emotional speech using spectral pattern features', , 2(1), pp. 53-61. doi: 10.22044/jadm.2014.150
Harimi, Ali, Shahzadi, Ali, Ahmadyfard, Alireza, Yaghmaie, Khashayar. Classification of emotional speech using spectral pattern features. , 1393; 2(1): 53-61. doi: 10.22044/jadm.2014.150

Classification of emotional speech using spectral pattern features

Journal of AI and Data Mining
مقاله 19، دوره 2، شماره 1، خرداد 2014، صفحه 53-61    اصل مقاله (690.8 K)
نوع مقاله: Original/Review Paper
شناسه دیجیتال (DOI): 10.22044/jadm.2014.150
نویسندگان
Ali Harimi* 1؛ Ali Shahzadi1؛ Alireza Ahmadyfard2؛ Khashayar Yaghmaie1
1Faculty of Electrical & Computer Engineering, Semnan University
2Department of Electrical Engineering and Robotics, Shahrood University of technology
چکیده
Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition. These features extracted from the spectrogram of speech signal using image processing techniques. For this purpose, details in the spectrogram image are firstly highlighted using histogram equalization technique. Then, directional filters are applied to decompose the image into 6 directional components. Finally, binary masking approach is employed to extract SPs from sub-banded images. The proposed HEs are also extracted by implementing the band pass filters on the spectrogram image. The extracted features are reduced in dimensions using a filtering feature selection algorithm based on fisher discriminant ratio. The classification accuracy of the pro-posed SER system has been evaluated using the 10-fold cross-validation technique on the Berlin database. The average recognition rate of 88.37% and 85.04% were achieved for females and males, respectively. By considering the total number of males and females samples, the overall recognition rate of 86.91% was obtained.
کلیدواژه‌ها
Speech emotion recognition؛ spectral pattern features؛ harmonic energy features؛ cross validation
آمار
تعداد مشاهده مقاله: 3,142
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