The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Model) is a finite mixture probability distribution model. Although the two models have a close relationship, they are always discussed independently and separately. The EM (Expectation-Maximum) algorithm is a general me...

The current paper proposes skew Gaussian mixture models for speaker recognition and an associated algorithm for its training from experimental data. Speaker identification experiments were conducted, in which speakers were modeled using the familiar Gaussian mixture models (GMM), and the new skewGMM. Each model type was evaluated using two sets of feature vectors, the mel-frequency cepstral coe...

Automatic dialect classification has gained interests in the field of speech research because it is important to characterize speaker traits and to estimate knowledge that could improve integrated speech technology (e.g., speech recognition, speaker recognition). This study addresses novel advances in unsupervised spontaneous Latin American Spanish dialect classification. The problem considers ...

One-to-many eigenvoice conversion (EVC) allows the conversion of a specific source speaker into arbitrary target speakers. Eigenvoice Gaussian mixture model (EV-GMM) is trained in advance with multiple parallel data sets consisting of the source speaker and many pre-stored target speakers. The EV-GMM is adapted for arbitrary target speakers using only a few utterances by estimating a small numb...

Discriminative training (DT) methods of acoustic models, such as SVM and MMI-training GMM, have been proved effective in spoken language recognition. In this paper we propose a DT method for GMM using the large margin (LM) estimation. Unlike traditional MMI or MCE methods, the LM estimation attempts to enhance the generalization ability of GMM to deal with new data that exhibits mismatch with t...

This paper describes a method for determining the vocal tract spectrum from articulatory movements using a Gaussian Mixture Model (GMM) to synthesize speech with articulatory information. The GMM on joint probability density of articulatory parameters and acoustic spectral parameters is trained using a parallel acousticarticulatory speech database. We evaluate the performance of the GMM-based m...

Fitting a Gaussian mixture model (GMM) to the smoothed speech spectrum allows an alternative set of features to be extracted from the speech signal. These features have been shown to possess information complementary to the standard MFCC parameterisation. This paper further investigates the use of these GMM features in combination with MFCCs. The extraction and use of a confidence metric to com...

This paper describes the voice conversion based on the Mixtures of Factor Analyzers (MFA) which can provide an efficient modeling with a limited amount of training data. As a typical spectral conversion method, a mapping algorithm based on the Gaussian Mixture Model (GMM) has been proposed. In this method two kinds of covariance matrix structures are often used : the diagonal and full covarianc...

Gaussian Mixture Models (GMMs) in combination with Support Vector Machine (SVM) classifiers have been shown to give excellent classification accuracy in speaker recognition. In this work we use this approach for language identification, and we compare its performance with the standard approach based on GMMs. In the GMM-SVM framework, a GMM is trained for each training or test utterance. Since i...

This paper presents an interval version of the Geometric Machine Model (GMM) and the programming language induced by its structure. The GMM is an abstract machine model, based on Girard’s coherence space, capable of modelling sequential, alternative, parallel (synchronous) and non-deterministic computations on a (possibly infinite) shared memory. The processes of the GMM are inductively constru...