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 inter-mingled 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 Neuro-Fuzzy 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.