In this paper, we focus on distributed speaker recognition, a technique in which quantized feature parameters are sent to a server, as with distributed speech recognition. The Gaussian mixture model , the traditional method used for speaker recognition, is trained using the maximum likelihood approach. The GMM has output probability functions with continuous density functions. It is difficult t...

Speaker recognition (SRE), also called as voiceprint recognition, is the problem of determining the identity of the speaker from a sample of speech signal. It is an important branch of speech signal processing and has many potential applications such as in telephone banking, access control, information security, law enforcement and other forensic applications (Bimbot et al., 2004; Campbell Jr.,...

In this paper, the statistical method of Factor Analysis(FA) is studied on Gaussian Mixture Model(GMM) based speaker identification(SI) system to model the data covariance which is usually neglected due to the training data sparseness. Because the variance of GMM can represents speaker variability, it is very important in SI systems. By FA modeled the data covariance, a relative gain of 39.6% o...

In this letter, we present a speech enhancement technique based on the ambient noise classification incorporating the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are chosen as different values a...

The NAM-to-speech conversion proposed by Toda and colleagues which converts Non-Audible Murmur (NAM) to audible speech by statistical mapping trained using aligned corpora is a very promising technique, but its performance is still insufficient, mainly due to the difficulty in estimating F0 of the transformed voice from unvoiced speech. In this paper, we propose a method to improve F0 estimatio...

To model the speech utterance at a finer granularity, this paper presents a novel state-alignment based supervector modeling method for text-independent speaker verification, which takes advantage of state-alignment method used in hidden Markov model (HMM) based acoustic modeling in speech recognition. By this way, the proposed modeling method can convert a text-independent speaker verification...

The paper considers text independent speaker identification over the telephone using short training and testing data. Gaussian Mixture Modeling (GMM) is used in the testing phase, but the parameters of the model are taken from clusters obtained for the training data by an adequate choice of feature vectors and a distance measure without optimization in the maximum likelihood (ML) sense. This di...

In this paper, we propose a method to analyze gender of the pedestrian and whether he or she has a baggage or not in a public space. The challenging part of this work is we only use top-view camera images to protect the pedestrians’ privacy. We focused on temporal changes in their position, shape, and contours over the frames because their appearances do not provide much information. We extract...

In this paper, we describe a statistical approach to both an articulatory-to-acoustic mapping and an acoustic-to-articulatory inversion mapping without using phonetic information. The joint probability density of an articulatory parameter and an acoustic parameter is modeled using a Gaussian mixture model (GMM) based on a parallel acoustic-articulatory speech database. We apply the GMM-based ma...