In this paper we explore the use of non-linear transformations in order to improve the performance of an entropy based voice activity detector (VAD). The idea of using a non-linear transformation comes from some previous work done in speech linear prediction (LPC) field based in source separation techniques, where the score function was added into the classical equations in order to take into a...

Voice activity detection (VAD) is a basic component of noise reduction algorithms. In this paper, we propose a voice activity detector based on a sequential Gaussian Mixture Model (SGMM) in log-spectral domain. This model comprises two Gaussian components, which respectively describe the speech and nonspeech log-power distributions. The initial distributions are firstly established by EM algori...

In this paper, we propose a robust voice activity detection method based on long-term stationarity (LTS) of the speech signal. The approach is motivated by the fact that noise, in timedomain, is relatively more stationary as compared to speech. We describe the use of Linear dynamic models (LDMs) as a measure of calculating the long-term stationarity of the signal and propose a voice activity de...

In this paper a variety of front-end configurations are evaluated on Hungarian telephone speech databases. Our aim was to measure directly the efficiency of the front-ends on real noisy and normal speech data. As a baseline the ETSI ADSR standard front-end is used. Some simplification on the standard is introduced resulting in better performance on our databases than the original front-end in t...

We describe a voice activity detection algorithm which leads to significant improvement of a narrow-bandwidth speaker verification system under harsh environments. This algorithm is based on a time-scale feature which is extracted from wavelet subbands. A statistical quantile filtering technique is proposed to estimate an adaptive noise threshold. A hang-over scheme is then applied to bridge sh...

In this paper we propose an effective, robust and computationally low-cost HMM-based start-endpoint detector for speech recognisers. Our first attempts follow the classical scheme feature extractor-Viterbi classifier (used for voice activity detection), followed by a post-processing stage, but the ultimate goal we pursue is a pure HMM-based architecture capable of performing the endpointing tas...

In this paper, the feasibility of a system developed for the remote diagnosis of voice pathologies is analysed. More specifically, the performance of MFCC-based pathology detectors over speech transmitted through an analogue telephone channel is studied. Results indicate that MFCC are voice features fairly robust to amplitude distortion and almost insensitive to phase distortion, but the effici...

The emerging applications of wireless speech communication are demanding increasing levels of performance in noise adverse environments together with the design of high response rate speech processing systems. This is a serious obstacle to meet the demands of modern applications and therefore these systems often needs a noise reduction algorithm working in combination with a precise voice activ...

Partially observable Markov decision process (POMDP) has been generally used to model agent decision processes such as dialogue management. In this paper, possibility of applying POMDP to a voice activity detector (VAD) has been explored. The proposed system first formulates hypotheses about the current noise environment and speech activity. Then, it decides and observes the features that are e...