International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 8, No. 11, 8 Nov. 2016
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Efficient Modelling Technique based Speaker Recognition under Limited Speech Data
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Author(s)
Index Terms
Vector Quantization, Fuzzy c-means Vector Quantization, Fuzzy Vector Quantization2, Novel Fuzzy Vector Quantization, Objective Function
Abstract
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.
Cite This Paper
Satyanand Singh, Abhay Kumar, David Raju Kolluri,"Efficient Modelling Technique based Speaker Recognition under Limited Speech Data", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.11, pp.41-48, 2016. DOI: 10.5815/ijigsp.2016.11.06
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