Mixture HMMs Research Articles

Speaker and Channel Factors in Text-Dependent Speaker Recognition

We reformulate joint factor analysis so that it can serve as a feature extractor for text-dependent speaker recognition. The new formulation is based on left-to-right modeling with tied mixture HMMs and it is designed to deal with problems such as the inadequacy of subspace methods in modeling speaker-phrase variability, UBM mismatches that arise as a result of variable phonetic content, and the need to exploit text-independent resources in text-dependent speaker recognition. We pass the features extracted by factor analysis to a trainable backend which plays a role analogous to that of PLDA in the i-vector/PLDA cascade in text-independent speaker recognition. We evaluate these methods on a proprietary dataset consisting of English and Urdu passphrases collected in Pakistan. By using both text-independent data and text-dependent data for training purposes and by fusing results obtained with multiple front ends at the score level, we achieved equal error rates of around 1.3% and 2% on the English and Urdu portions of this task.

Facial expression recognition based on combined HMM

In this paper, a new recognition method called the combined HMM is proposed. The method combines HMM and Mixture-HMM through KNN. Mixture HMMs are trained on any two classes of training samples with traditional HMMs. The recognition procedure of combined HMM is to make a final class judgement by KNN while the HMM's basic decision is different from the Mixture-HMM confirmatory sentence. At the same time, some improvements for the KNN decision criterion and the HMM interactive rule are made to enhance classification ability. Experiments show that the performance of this combined method exceeds that of HMM.

A Study of Variable-Parameter Gaussian Mixture Hidden Markov Modeling for Noisy Speech Recognition

To improve recognition performance in noisy environments, multicondition training is usually applied in which speech signals corrupted by a variety of noise are used in acoustic model training. Published hidden Markov modeling of speech uses multiple Gaussian distributions to cover the spread of the speech distribution caused by noise, which distracts the modeling of speech event itself and possibly sacrifices the performance on clean speech. In this paper, we propose a novel approach which extends the conventional Gaussian mixture hidden Markov model (GMHMM) by modeling state emission parameters (mean and variance) as a polynomial function of a continuous environment-dependent variable. At the recognition time, a set of HMMs specific to the given value of the environment variable is instantiated and used for recognition. The maximum-likelihood (ML) estimation of the polynomial functions of the proposed variable-parameter GMHMM is given within the expectation-maximization (EM) framework. Experiments on the Aurora 2 database show significant improvements of the variable-parameter Gaussian mixture HMMs compared to the conventional GMHMMs

Estimating high‐confidence portions based on agreement among outputs of multiple LVCSR models

AbstractThis paper experimentally evaluates the agreement among the outputs of multiple Japanese LVCSR models, with respect to whether it is effective as an estimate of confidence for each hypothesized word. The results of experimental evaluation show that the agreement between the outputs with two LVCSR models with different decoders and acoustic models can achieve quite reliable confidence. Furthermore, among various features of acoustic models based on Gaussian mixture HMMs, it is concluded that ones such as whether or not to have short pause models, as well as different units in HMMs are the most effective in achieving highly reliable confidence. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(7): 33–40, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10636

Tree‐based clustering for gaussian mixture HMMs

AbstractTree‐based clustering is an effective method for sharing the state of an HMM in which clustering is applied to a set of context‐dependent models with the phoneme context as the splitting condition. In past papers, the method has been restricted to the single Gaussian HMM. The single Gaussian HMM, however, is insufficient for representing the acoustic features, and an adequate topology (sharing of HMM state) will not necessarily be realized. Furthermore, in order to arrive at a state‐sharing model with the desired number of mixtures, the process of doubling the number of mixtures and the embedded training must be iterated after the tree‐based clustering, which increases the time for training. Consequently, this paper proposes a method in which the tree‐based clustering algorithm for the single Gaussian HMM is extended to the clustering of the mixed Gaussian HMM. The proposed method reduces the training time to approximately one‐third that of the conventional method of handling the single Gaussian HMM. A recognition experiment using a phone typewriter and a recognition experiment for continuous word demonstrate that the recognition rate is improved by one to two points. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(4): 40–49, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1118

Phonemic hidden Markov models with continuous mixture output densities for large vocabulary word recognition

The authors demonstrate the effectiveness of phonemic hidden Markov models with Gaussian mixture output densities (mixture HMMs) for speaker-dependent large-vocabulary word recognition. Speech recognition experiments show that for almost any reasonable amount of training data, recognizers using mixture HMMs consistently outperform those employing unimodal Gaussian HMMs. With a sufficiently large training set (e.g. more than 2500 words), use of HMMs with 25-component mixture distributions typically reduces recognition errors by about 40%. It is also found that the mixture HMMs outperform a set of unimodal generalized triphone models having the same number of parameters. Previous attempts to employ mixture HMMs for speech recognition proved discouraging because of the high complexity and computational cost in implementing the Baum-Welch training algorithm. It is shown how mixture HMMs can be implemented very simply in unimodal transition-based frameworks by allowing multiple transitions from one state to another. >