Improved Speaker Verification through Pro
نویسنده
چکیده
In this paper we propose a new adaptation technique for improved text-independent speaker verification with limited amounts of training data using Gaussian mixture models (GMMs). The technique, referred to as probabilistic subspace adaptation (PSA), employs a probabilistic subspace description of how a client’s parametric representation (i.e. GMM) is allowed to vary. Our technique is compared to traditional maximum a posteriori (MAP) adaptation, or relevance adaptation (RA), and maximum likelihood eigen-decomposition (MLED), or subspace adaptation (SA) techniques. Results are given on a subset of the XM2VTS databases for the task of textindependent speaker verification.
منابع مشابه
Using Exciting and Spectral Envelope Information and Matrix Quantization for Improvement of the Speaker Verification Systems
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Speaker verification from talking a few words of sentences has many applications. Many methods as DTW, HMM, VQ and MQ can be used for speaker verification. We applied MQ for its precise, reliable and robust performance with computational simplicity. We also used pitch frequency and log gain contour for further improvement of the system performance.
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