In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical modelbased 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 set according to the class...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy GAUSSIAN MIXTURE MODEL BASED SYSTEM IDENTIFICATION AND CONTROL By Jing Lan August 2006 Chair: José C. Principe Major Department: Electrical and Computer Engineering In this dissertation, we present a methodology of combining an improved ...

Speaker variability is a well-known problem of state-of-theart Automatic Speech Recognition (ASR) systems. In particular, handling children speech is challenging because of substantial differences in pronunciation of the speech units between adult and child speakers. To build accurate ASR systems for all types of speakers Hidden Markov Models with Gaussian Mixture Densities were intensively use...

s of forty-nine regular papers from PAKDD 2005 [Ho et al. 2005], which were not used in the framework building process, were collected and analyzed to see if they fit in the categories identified by grounded theory. The abstract of each article was analyzed to identify the primary objective(s) the author(s) are addressing. Take the article “Adjusting Mixture Weights of Gaussian Mixture Model vi...

This paper presents an approach for speaker diarization based on a novel combination of Gaussian mixture model (GMM) and standard Bayesian information criterion (BIC). Gaussian mixture model provides a good description of feature vector distribution and BIC enables a proper merging and stopping criterion. Our system combines the advantage of these two method and yields favorable performance. Ex...

In this letter, we propose an efficient method to improve the performance of voiced/unvoiced (V/UV) sounds decision for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). We first present an effective analysis of the features and the classification method adopted in the SMV. And feature vectors which are applied to the GMM are then selected from relevant paramete...

In speaker diarization, the speech/voice activity detection is performed to separate speech, non-speech and silent frames. Zero crossing rate and root mean square value of frames of audio clips has been used to select training data for silent, speech and non-speech models. The trained models are used by two classifiers, Gaussian mixture model (GMM) and Artificial neural network (ANN), to classi...

In this paper, methods of Gaussian Mixture Model (GMM) are presented for both silence/voiced/voiceless segmentation and tone decision in Mandarin continuous speech recognition system. GMM has been used for silence/voiced/voiceless segmentation before, but the feature parameters can be modified to improve both accuracy and speed. As a popular method in pattern recognition, GMM is first proposed ...

In this paper we aim to improve the performance of Gaussian Mixture Model (GMM) classifier using Impostor model parameters for a closed set Speaker Identification task. We propose a novel method of speaker model training which uses the parameters of an Impostor Model to discriminatively train, in order to improve the performance of the GMM based classifier. This is unlike conventional technique...

Localization algorithms based on received signal strength indication (RSSI) are widely used in the field of target localization due to its advantages of convenient application and independent from hardware devices. Unfortunately, the RSSI values are susceptible to fluctuate under the influence of non-line-of-sight (NLOS) in indoor space. Existing algorithms often produce unreliable estimated di...