Keyword Detection using Auto Associative Neural Network with Reference to Assamese Language
Deepjyoti Kalita1, Khurshid Alam Borbora2
1Deepjyoti Kalita, Computer Science & IT, Mangaldai College,Mangaldai, India.
2Khurshid Alam Borbora, Department of Computer Science, IDOL, Gauhati University.
Manuscript received on 1 August 2019. | Revised Manuscript received on 7 August 2019. | Manuscript published on 30 September 2019. | PP: 3290-3294 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5428098319/2019©BEIESP | DOI: 10.35940/ijrte.C5428.098319
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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: Speech and speaker recognition has yet not achieved the state-of-the-art position. Keyword detection in audio clips is gaining importance as it contributes to the audio recognition and detection systems. In this area, very few works have been carried out. In this paper, we present our experiment on keyword detection within recorded news clips. It is based on Assamese language spoken by Assamese native speakers. For this experiment, the audio clips are collected from local TV news debates, whereas the keywords are recorded by random speakers. The keywords are selected for recording considering the fact that they appear somewhere within the audio clips for a finite number of times. Mel Frequency Cepstral Coefficient (MFCC) is considered as feature and Auto Associative Neural Network (AANN) is considered as the classifier tool. With this detection model an average accuracy of 87% is achieved.
Keyword: AANN, Confusion Matrix, FPR, KWS, MFCC, TPR
Scope of the Article: Neural Information Processing