مشخصات پژوهش

عنوان	Speech Emotion Recognition Using a Hierarchical Adaptive Weighted MultiLayer Sparse Auto-Encoder Extreme Learning Machine with New Weighting and Spectral/SpectroTemporal Gabor Filter Bank Features
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Speech Emotion Recognition, ELM
چکیده	The importance of doing research into affective computing has multiplied with the growing popularity of intelligent and human-machine interface systems. In this paper, a system for speech emotion recognition (SER) is proposed using new techniques in different parts. The given system extracts speech features from both speech and glottal-waveform signals in feature extraction section including spectro-temporal ones obtained from Gabor filter bank (GBFB) and separate Gabor filter bank (SGBFB) which have not been so far utilized for SER. At the classification step, a hierarchical adaptive weighted multilayer extreme learning machine (H-AWELM) is employed. This hybrid classifier consists of two parts: the first part for sparse unsupervised feature learning using a multi-layer neural network (NN) with sparse extreme learning machine auto-encoder (ELMAE) layers, and the second part for feature classification in the last layer using Tikhonov’s regularized least squares (LS) technique. One of the most important issues in multi-class ELM training process is how to deal with data imbalance problem. This paper presents a new adaptive weighting method to solve this problem that can be more accurate than current weighting methods. Finally, the proposed system is evaluated on a well-known emotional speech database. Experimental results demonstrate that the proposed system outperforms the state-of-the-art ones
پژوهشگران	سیدجهانشاه کبودیان (نفر دوم)، فاطمه دانشفر (نفر اول)