Efficient Modelling Technique based Speaker Recognition under Limited Speech Data

As on date, Speaker-specific feature extraction and modelling techniques has been designed in automatic speaker recognition (ASR) for a sufficient amount of speech data. Once the speech data is limited the ASR performance degraded drastically. ASR system for limited speech data is always a highly challenging task due to a short utterance. The main goal of ASR to form a judgment for an incoming speaker to the system as being which member of registered speakers. This paper presents a comparison of three different modelling techniques of speaker specific extracted information (i) Fuzzy c-means (FCM) (ii) Fuzzy Vector Quantization2 (FVQ2) and (iii) Novel Fuzzy Vector Quantization (NFVQ). Using these three modelling techniques, we developed a text independent automatic speaker recognition system that is computationally modest and equipped for recognizing a non-cooperative speaker. In this investigation, the speaker recognition efficiency is compared to less than 2 sec of text-independent test and train utterances of Texas Instruments and Massachusetts Institute of Technology (TIMIT) and self-collected database. The efficiency of ASR has been improved by 1% with the baseline by hiding the outliers and assigns them by their closest codebook vectors the efficiency of proposed modelling techniques is 98.8%, 98.1% respectively for TIMIT and self-collected database.