Speaker Identification by Comparison of Smart Methods
Ali mahdavi Meimand
- Department of Electrical Engineering, Sirjan Branch Islamic Azad University,Sirjan, Iran.
- Department of Computer Engineering, Sirjan Branch Islamic Azad University,Sirjan, Iran.
- Department of Electrical Engineering, Shahid Bahonar University of Kerman.
Voice recognition or speaker identification is a topic in artificial intelligence and computer science that aims to identify a person based on his voice. Speaker identification is a scientific field with numerous applications in various fields including security, espionage, etc. There are various analyses to identify the speaker in which some characteristics of an audio signal are extracted and these characteristics and a classification method are used to identify the specified speaker among many other speakers. The errors in the results of these analyzes are inevitable; however, researchers have been trying to minimize the error by modifying the previous analyzes or by providing new analyzes. This study uses the modification of group delay function analysis for the first time to identify the speaker. The results obtained by this method, in comparison with the group delay function method, approve the capabilities of the proposed method.
Share and Cite
Ali mahdavi Meimand, Amin Asadi, Majid Mohamadi, Speaker Identification by Comparison of Smart Methods, Journal of Mathematics and Computer Science, 10 (2014), no. 1, 61-71
Meimand Ali mahdavi, Asadi Amin, Mohamadi Majid, Speaker Identification by Comparison of Smart Methods. J Math Comput SCI-JM. (2014); 10(1):61-71
Meimand, Ali mahdavi, Asadi, Amin, Mohamadi, Majid. "Speaker Identification by Comparison of Smart Methods." Journal of Mathematics and Computer Science, 10, no. 1 (2014): 61-71
- Speaker identification
- MFCC analysis
- MODGDF analysis
- Auto parameters.
Richard Duncan, A Description And Comparison Of The Feature Sets Used In Speech Processing Ph , Mississippi State University, (), 325-3149.
Tomi Kinnunen, Spectral Features for Automatic Text-IndependentSpeaker Recognition, LICENTIATE’STHESIS University of Joensuu Department of Computer Science P.O. Box 111, FIN-80101 Joensuu, Finland (2003)
Rangsit Campus, Klongluang, Pathum-thani, Voice Articulator for Thai Speaker Recognition, Thammasat Int. J. Sc. Tech., Vol.6, No.3 (2001)
Antanas LIPEIKA, Joana LIPEIKIEN ÿ E, Laimutis TELKSNYS, Development of Isolated Word Speech Recognition System, , (2001)
Rangsit Campus, Pathum-thani Klongluang, Voice Articulator for Thai Speaker Recognition, Thammasat Int. J. Sc. Tech., Vol.6, No.3 (2001)
Tomi Kinnunen, Haizhou Li , An overview of text-independent speaker recognition: From features to supervectors, Speech Communication , 52 (2010), 12–40.
Richard Petersens Plads , Mel Frequency Cepstral Coefficients: An Evaluation of Robustness of MP3 Encoded Music., Informatics and Mathematical Modeling Technical University of Denmark Richard Petersens Plads - Building 321 DK-2800 Kgs. Lyngby, Denmark (2002)
Hat Yai, MODIFIED MEL-FREQUENCY CEPSTRUM COEFFICIENT, Department of Computer Engineering Faculty of Engineering Prince of Songkhla University Hat Yai, Songkhla Thailand (90112)
Rajesh Ramya, M. Hegde, Hema A. Murthy. , Significance of Group Delay based Acoustic Features in the Linguistic Search Space for Robust Speech Recognition, Indian Institute of Technology Madras, Chennai, India. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India (2008)
M. Rajesh, Hema Hegde, Significance of the Modified Group Delay Feature in Speech Recognition, , (2007)
C. F. Chen, L. S. Shieh, A Novel Approach to Linear Model Simplification, International Journal of Control., 8 (1968), 561 – 570.
G. Parmer, R. Prasad, S. Mukherjee, Order Reduction of Linear Dynamic Systems using Stability Equation Method and GA, World Academy of Science, Engineering and Technology, 26 (2007), 72 - 78.
Adjoudj Réda, Boukelif Aoued , Artificial Neural Network & Mel-Frequency Cepstrum Coefficients-Based Speaker Recognition, Evolutionary Engineering and Distributed Information Systems Laboratory,EEDIS, Computer Science Department, University of Sidi Bel-Abbès, Algeria March, (2005), 27-31
Julien Neel , Cluster analysis methods for speech recognition, Department of Speech, Music and Hearing Royal Institute of Technology S-100 44 Stockholm (2005)