Multilingual Speaker Recognition Using ANFIS
Feature based Recognition Systems has been an area of intense research for long. The creation of a reliable, robust and sufficiently efficient recognition system has been tried using features from several sources including textual and image sources. Speech based sources have also been used for the creation of such a recognition system. However, variations caused due to differences in individual speaker characteristics, mood variations and intermingled noise disturbances make the realization of such a system very difficult. This paper proposes a recognition system for identification of the speaker, language and the words spoken. The system makes use of Adaptive Neuro-Fuzzy Inference paradigm for the same. First, the sampling frequency and the speech features are extracted from the speech database to form speech feature vectors. The features used are LPC, LPCC, RC, LAR, LSF and ARSCIN. The speech database is prepared using 25 speakers including male and female speakers. Five different speaking texts of different languages having same meaning are used to get the best speaker identification accuracy. The languages spoken by the speakers include English, Hindi, Punjabi, Sanskrit and Telugu. The Feature vectors, thus prepared, are fed to an Adaptive NeuroFuzzy Inference System for speaker, language and word recognition. The experimental results show the system to be amply efficient and successful in the recognition tasks involved.
Biometics Multilingual Speaker Recognition Speaker Identification Speaker Verification Adaptive Neuro-Fuzzy System (ANFIS).
Bipul Pandey Alok Ranjan Rajeev Kumar Anupatn Shukla
Department of Information Technology ABV- Indian Institute of Information Technology and Management Gwalior, India
国际会议
2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)
大连
英文
2395-2399
2010-07-05（万方平台首次上网日期，不代表论文的发表时间）