Speech endpoint detection continues to be a challenging problem particularly for speech recognition in noisy environments. In this paper, we address this problem from the point of view of fractals and chaos. By studying recurrence time statistics for chaotic systems, we find the nonstationarity and transience in a time series are due to non-recurrence and lack of fractal structure in the signal...

To investigate the electrical changes observed at plasma etching endpoints, experiments were performed in an inductively coupled, rf-biased reactor, during CF4/Ar plasma etches of silicon dioxide films on silicon substrates. The rf bias current, voltage, and impedance were measured vs. time during etching. Simultaneously, a Langmuir probe measured the electron and ion densities, ion current den...

We investigate two statistical detection problems in plasma etch endpoint detection: change-point detection and pattern matching. Our approach is based on a segmental semi-Markov model framework. In the change-point detection problem, the change-point corresponds to state switching in the model. For pattern matching, the pattern is approximated as a sequence of linear segments that are modeled ...

This paper is concerned with the robust endpoint detection and noisy speech recognition over wireless network. Firstly, the MLP-based and GMM-based endpoint detection incorporated with data contamination and continuous spectral subtraction techniques were investigated. Then, for noisy wireless speech recognition, a combined technique of data contamination and robust training was proposed to sep...

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...

Shown is an effective speech endpoint detection algorithm using a trained support vector machine (SVM) and a feature vector including contextual information speech features. With this and other innovations the proposed algorithm yields high discrimination and reports significant improvements over standard methods and algorithms defining the decision rule in terms of averaged subband speech feat...

In this paper, a fractal based approach is proposed to detect endpoints in whispered speech. The underlying principle is based on the fact that whispered speech is sufficiently chaotic and thus can be analyzed using fractal theory. Due to the different scope of fractal dimensions of silence, noise and speech segment, speech/non-speech segment could be determined from that with a simple decision...

This paper describes the database development of spoken Hindi Hybrid words. The main focus is on the analysis of spoken Hindi Hybrid Words by using endpoint detection algorithm. The development is done in the robust environment. Male and Female speakers are selected from different states to analyze the acoustical variation in their utterances. Various emotions such as normal, happy, anger and s...

This paper describes a novel noise robust speech detection algorithm that can operate reliably in severe car noisy conditions. High performance has been obtained with the following techniques: (1) noise suppression based on principal component analysis for pre-processing, (2) robust endpoint detection using dynamic parameters [1] and (3) speech verification using periodicity of voiced signals w...

Abstract This paper investigates in-situ thermographic imaging of a CMP pad during polish to extract spatial endpoint uniformity and polish rate uniformity across the wafer. IR images of the pad contain subtle temperature gradients masked by noise, making endpoint detection difficult. Also effects such as slurry flow, head oscillation, camera angle, and a baseline temperature increase in the pa...