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Speaker detection is an important component of a speech-based user interface. Audiovisual speaker detection, speech and speaker recognition or speech synthesis for example find multiple applications in human-computer interaction, multimedia content indexin ...
The goal of this thesis is to develop and design new feature representations that can improve the automatic speech recognition (ASR) performance in clean as well noisy conditions. One of the main shortcomings of the fixed scale (typically 20-30 ms long ana ...
Speaker recognition systems achieve acceptable performance in controlled laboratory conditions. However, in real-life environments, the performance of a speaker recognition system degrades drastically, the principal cause being the mismatch that exists bet ...
In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the ...
This project develops the new model Harmonic Plus Noise applied for the concatenative speech synthesis. The software is composed of an analysis part (off-line process) applied on the first initial database and a synthesis part (real time process) applied o ...
Speech recognition applications embedded on a PDA are already available on the market. The usual hardware for this kind of systems is a single microphone mounted on the PDA, giving good results within quiet environments. Though, the recognition rate falls ...
This paper describes an approach where posterior-based features are applied in those recognition tasks where the amount of training data is insufficient to obtain a reliable estimate of the speech variability. A template matching approach is considered in ...
Speaker verification is a biometric identity verification technique whose performance can be severely degraded by the presence of noise. Using a coherent notation, we reformulate and review several methods which have been proposed to quantify the uncertain ...
This paper addresses the problem of sensing or recovering a signal s, captured by distributed low-complexity sensors. Each sensor observes a noisy version of the signal of interest, and independently forms an approximant of its observation. This approximan ...
In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the ...