We present an approach to speaker recognition in the textindependent domain of conversational telephone speech using a text-constrained system designed to employ select highfrequency keywords in the speech stream. The system uses speaker word models generated via Hidden Markov Models (HMMs) — a departure from the traditional Gaussian Mixture Model (GMM) approach dominant in text-independent wor...

in this paper, a new method is presented for the detection of defects in random textures. in the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of gabor wavelet filters, and their probability density is estimated by means of the gaussian mixture model (gmm). in the testing stage, similar to the previous stage,at  first, the feature...

In this paper, we present a new method for isolated keyword detection that is meant to activate a personal device from standby state. Instead of using the common method for speech recognition such as Hidden Markov Model (HMM) or Dynamic Time Warping (DTW), we modify a GMM-UBM (Gaussian Mixture Model – Universal Background Model) scheme that is better known in speaker recognition field. Since on...

A Successive State and Mixture Splitting (SSMS) algorithm for optimizing the size of models used in speech recognition for small size of mobile devices is proposed in this paper. The proposed algorithm employs essentially Continuous Hidden Markov Model (CHMM) structure and this CHMM consists of variable parameter topology in order to minimize the number of model parameters and to reduce recogni...

A new phone level hidden Markov model approach applied to human humming transcription is proposed in this research. A music note has two important attributes, i.e. pitch and duration. The proposed system generates multidimensional humming transcriptions, which contain both pitch and duration information. Query by humming provides a natural means for content-based retrieval from music databases,...

The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently led to a resurgence in the use of Artificial Neural Network Hidden Markov Model (ANN/HMM) hybrid systems for Automatic Speech Recognition (ASR). In this paper we report results of a DBN-pretrained context-dependent ANN/HMM system trained on two datasets that are much larger than any reported previously with DBN-pretr...

In this paper, we propose a Correlation-Maximization denoising filter which utilizes periodicity information to remove additive noise in bird calls. We also developed a statistically-based noise robust bird-call classification system which uses the denoising filter as a frontend. Enhanced bird calls which are the output of the denoising filter are used for feature extraction. Gaussian Mixture M...

Emotion expressions sometimes are mixed with the utterance expression in spontaneous face-to-face communication, which makes difficulties for emotion recognition. This article introduces the methods of reducing the utterance influences in visual parameters for the audio-visual-based emotion recognition. The audio and visual channels are first combined under a Multistream Hidden Markov Model (MH...