نتایج جستجو برای: گشتاور تعمیمیافته gmm

تعداد نتایج: 7807  

2009
Guoli Ye Brian Kan-Wing Mak Man-Wai Mak

Most of current state-of-the-art speaker verification (SV) systems use Gaussian mixture model (GMM) to represent the universal background model (UBM) and the speaker models (SM). For an SV system that employs log-likelihood ratio between SM and UBM to make the decision, its computational efficiency is largely determined by the GMM computation. This paper attempts to speedup GMM computation by c...

2004
Tomoki Toda Alan W. Black Keiichi Tokuda

This paper describes the acoustic-to-articulatory inversion mapping using a Gaussian Mixture Model (GMM). Correspondence of an acoustic parameter and an articulatory parameter is modeled by the GMM trained using the parallel acousticarticulatory data. We measure the performance of the GMMbased mapping and investigate the effectiveness of using multiple acoustic frames as an input feature and us...

1999
Mouhamadou Seck Frédéric Bimbot Didier Zugaj Bernard Delyon

We present a technique for the segmention of a sound track into two classes of segments. Each frame of signal is preprocessed by extracting cepstral coefficients and their first order derivatives. For each class, the distribution of the frame parameter vectors is modeled by a Gaussian Mixture Model (GMM). GMM order is selected using two criteria : the Minimum Description Length (MDL) criterion ...

2003
Fabien Cardinaux Conrad Sanderson Sébastien Marcel

We compare two classifier approaches, namely classifiers based on Multi Layer Perceptrons (MLPs) and Gaussian Mixture Models (GMMs), for use in a face verification system. The comparison is carried out in terms of performance, robustness and practicability. Apart from structural differences, the two approaches use different training criteria; the MLP approach uses a discriminative criterion, wh...

2001
Douglas E. Sturim Douglas A. Reynolds Elliot Singer Joseph P. Campbell

This paper introduces the technique of anchor modeling in the applications of speaker detection and speaker indexing. The anchor modeling algorithm is refined by pruning the number of models needed. The system is applied to the speaker detection problem where its performance is shown to fall short of the state-of-the-art Gaussian Mixture Model with Universal Background Model (GMM-UBM) system. H...

2007
Zhenyu Shan Yingchun Yang Ruizhi Ye

One of the largest challenges in speaker recognition is dealing with speaker-emotion variability problem. Nowadays, compensation techniques are the main solutions to this problem. In these methods, all kinds of speakers’ emotion speech should be elicited thus it is not user-friendly in the application. Therefore the basic problem is how to get the distribution of speakers’ emotion speech and ho...

2015
Hao Zheng Shanshan Zhang Wenju Liu

This work explores the use of DNN/RNN for extracting Baum-Welch sufficient statistics in place of the conventional GMM-UBM in speaker recognition. In this framework, the DNN/RNN is trained for automatic speech recognition (ASR) and each of the output unit corresponds to a component of GMM-UBM. Then the outputs of network are combined with acoustic features to calculate sufficient statistics for...

Journal: :Pattern Recognition 2015
Michael Kemp Richard Y. D. Xu

This paper presents a framework to fit data to a model consisting of multiple connected ellipses. For each iteration of the fitting algorithm, the representation of the multiple ellipses is mapped to a Gaussian mixture model (GMM) and the connections are mapped to geometric constraints for the GMM. The fitting is a modified constrained expectation maximisation (EM) method on the GMM (maximising...

2011
Reda Jourani Khalid Daoudi Driss Aboutajdine

Gaussian mixture models (GMM), trained using the generative criterion of maximum likelihood estimation, have been the most popular approach in speaker recognition during the last decades. This approach is also widely used in many other classification tasks and applications. Generative learning in not however the optimal way to address classification problems. In this paper we first present a ne...

2010
Fadi Biadsy Julia Hirschberg Michael Collins

In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similaritie...

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