MFCC Based Speech Retrieval
Jyoti Srivastava1, Tanveer J. Siddiqui2, U. S. Tiwary3, Ashish Kumar Srivastava4
1Dr. Jyoti Srivastava, Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Chittoor District, Andhra Pradesh, India.
2Dr. Tanveer J. Siddiqui, J. k. Institute of Applied Department of Electronics & Communication, University of Allahabad, Allahabad, India.
3Prof. U. S. Tiwary, Indian Institute of Information Technology Allahabad, India.
4Dr. Ashish Kumar Srivastava, Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Chittoor District, Andhra Pradesh, India.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 601-606 | Volume-8 Issue-9, July 2019 | Retrieval Number: I7550078919/19©BEIESP | DOI: 10.35940/ijitee.I7550.078919
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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: This paper presents an approach for speech retrieval. The feature being used in this approach is MFCC. This approach does not use any phoneme recognizer or Speech to text tool hence it can be used for other languages as well leads to the problem of speech retrieval (SR). This method retrieves ranked audio files containing spoken text in response to a given speech query. In this paper indexing methods are described which represent the contents of the spoken documents. The indexing methods, which are based on the output of phoneme recognizer, take account of speech recognition errors. While in this paper, speech documents are directly compared with the speech query based on MFCC. Thus, reduced the overhead of conversion from speech to text.
Index Terms: MFCC, Phoneme Recognizer, Speech Retrieval, Speech Comparison.
Scope of the Article: Information Retrieval