We present a novel two-phase multi-attribute algorithm suitable for large surface mesh simplification. By employing a linear combination of error metrics to control the process, the proposed algorithm incorporates geometric error control and preserves other attributes of the original model such as the texture (vertex color) and surface normal. In the first phase, we utilize the volumesurface tr...

This article describes a new unsupervised methodology to learn F0 classes using HMM on a syllable basis. A F0 class is represented by a HMM with three emitting states. The unsupervised clustering algorithm relies on an iterative gaussian splitting and EM retraining process. First, a single class is learnt on a training corpus (8000 syllables) and it is then divided by perturbing gaussian means ...

The paper presents new clustering algorithm. The proposed algorithm gives less distortion as compared to well known Linde Buzo Gray (LBG) algorithm and Kekre’s Proportionate Error (KPE) Algorithm. Constant error is added every time to split the clusters in LBG, resulting in formation of cluster in one direction which is 135 in 2-dimensional case. Because of this reason clustering is inefficient...

This paper presents the LIMSI speaker diarization system for lecture data, in the framework of the Rich Transcription 2006 Spring (RT-06S) meeting recognition evaluation. This system builds upon the baseline diarization system designed for broadcast news data. The baseline system combines agglomerative clustering based on Bayesian information criterion with a second clustering using state-of-th...

Unsupervised cluster adaptive training of acoustic models offers promise in improving recognition accuracy, especially for speech recognition systems that store massive sets of speech samples from unknown people. How to classify the variety of acoustic characteristics is an important problem in adaptation sample clustering. We propose a novel speech sample clustering method that focuses on the ...

In this paper we study using the classi cation-based Bhattacharyya distance measure to guide biphone clustering. The Bhattacharyya distance is a theoretical distance measure between two Gaussian distributions which is equivalent to an upper bound on the optimal Bayesian classi cation error probability. It also has the desirable properties of being computationally simple and extensible to more G...

This article describes a new unsupervised methodology to learn F0 classes using HMM models on a syllable basis. A F0 class is represented by a HMM with three emitting states. The clustering algorithm relies on an iterative gaussian splitting and EM retraining process. First, a single class is learnt on a training corpus (8000 syllables) and it is then divided by perturbing gaussian means of suc...

In this paper, Fuzzy C-Means clustering with Expectation Maximization-Gaussian Mixture Model based hybrid modeling algorithm is proposed for Continuous Tamil Speech Recognition. The speech sentences from various speakers are used for training and testing phase and objective measures are between the proposed and existing Continuous Speech Recognition algorithms. From the simulated results, it is...

The following article describes two extensions to the \traditional" decision tree methods for clustering allophone HMM states in LVCSR systems. The rst, single tree clustering, combines all allophone states of all phones into a single tree. This can be used to improve performance for very small systems. The single tree clustering structure can also be exploited for speaker and channel adaptatio...