A Discrete Wavelet Based Approach to Speaker Identification for Different Speaking Styles.
Shanthini Pandiaraj1, T. Anita Jones Mary2
1Shanthini Pandiaraj, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
2T Anita Jones Mary Pushpa, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 285-288 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6048048419/19©BEIESP
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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 a speaker identification system using speech signals of different speaking styles. Normal and fast speaking styles have been chosen for the analysis. The speech data provided by the CHAINS corpus has been used for the experiment. The speech utterance used for testing is compared with the speech sample in the database. Two set ups, each consisting of three groups of speakers have been used for the experiment. From each of the setup, the information contained in the frequency ranges 300-4 KHz, 300-6 KHz and300-8KHz are extracted using the Daubechies db8 wavelet and the speaker identification accuracy is compared.
Keywords: Speaker Identification, Discrete Wavelet Transform
Scope of the Article: Machine Learning