We propose a low-memory-bandwidth, high-efficiency VLSI architecture for 60-k word real-time continuous speech recognition. Our architecture includes a cache architecture using the locality of speech recognition, beam pruning using a dynamic threshold, two-stage language model searching, a parallel Gaussian Mixture Model (GMM) architecture based on the mixture level and frame level, a parallel ...

Landmarks in speech signal are regions with abrupt spectral variations. Automated detection of these regions is important for several applications in speech processing. Performance of landmark detection using parameters extracted from predefined spectral bands generally gets limited by speaker related spectral variability. This paper presents a landmark detection technique which adapts to the a...

Speaker identification is an important activity in the process of speaker diarization. We need to model the speaker by Gaussian mixture model (GMM) for speaker identification purpose. Large GMM is called as a Universal Background Model (UBM) which is adapted into each speaker model for speaker identification purpose. This paper focuses on speech clustering for speaker diarization. The speaker d...

This paper describes a new speaker verification system based on orthogonal Gaussian mixture modeling (GMM) techniques combined with maximum a posteriori (MAP) adaptation. In most of the GMM based speaker verification systems, the variance of each component is constrained to be diagonal for its computational simplicity. However, this approximation inevitably introduces performance degradation. T...

Gaussian mixture model (GMM) is generally used to estimate the speaker model from speech for speaker identification. In this paper, we propose the method that estimates the optimal number of Gaussian mixtures based on incremental k-means for speaker identification. In the proposed method, the initialization with the optimal number of mixtures is done by adding dynamically the number of mixtures...

This paper presents a novel statistical sample-based approach for Gaussian Mixture Model (GMM)-based Voice Conversion (VC). Although GMM-based VC has the promising flexibility of model adaptation, quality in converted speech is significantly worse than that of natural speech. This paper addresses the problem of inaccurate modeling, which is one of the main reasons causing the quality degradatio...

This paper deals with Automatic Face Recognition (AFR), which means automatic identification of a person from a digital image. Our work focuses on an application for Czech News Agency that will facilitate to identify a person in a large database of photographs. The main goal of this paper is to propose some modifications and improvements of existing face recognition approaches and to evaluate t...

This paper presents a framework to fit data to a model consisting of multiple connected ellipses. For each iteration of the fitting algorithm, the representation of the multiple ellipses is mapped to a Gaussian mixture model (GMM) and the connections are mapped to geometric constraints for the GMM. The fitting is a modified constrained expectation maximisation (EM) method on the GMM (maximising...

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...