Gender Classification for Emotional Speech using GMFCC and Deep LSTM
Sandeep Kumar1, Jainath Yadav2
1Sandeep Kumar*, Lecturer, Department Of Computer Science, Government Polytechnic Banka, Banka, Bihar.
2Jainath Yadav,Asst. Professor, Department Of Computer Science, Central University Of South Bihar, Gaya, Bihar.
Manuscript received on November 17, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 3923-3928 | Volume-9 Issue-2, December 2019. | Retrieval Number: A6109119119/2019©BEIESP | DOI: 10.35940/ijitee.A6109.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We have come to the point that one of the important aspects of the process speech emotion recognition is the gender classification. The correct classification of gender will improve the performance of Speech Emotion Recognition (SER) system towards its robustness. Here, we are specifically referring to Gammatone Mel Frequency Cepstral Coefficient (GMFCC) as a feature extraction method that extracts features from IITKGPSESHC dataset, which is very crucial in deciding either male or female in gender classification. The well known classifier “Deep Long Short Term Memory (Deep LSTM)” is itself an important kind of Recurrent Neural Network (RNN) that handles the longrange dependencies more efficiently than the RNNs. The GMFCC feature has to pass through the Deep LSTM and get average percent gender identification accuracy of 98.3%.
Keywords: GMFCC, Deep LSTM, Gammatone Filter, ERB
Scope of the Article: Classification