Mixed Distance Measures for Optimizing Concatenative Vocabularies for Speech Synthesis: A Thesis Proposal

Synthesized speech from text-to-speech systems is generally produced from the concatenation of small units of speech. The concatenation process can be complex, involving smoothing and context dependent adjustments to the speech. The overall quality of the speech produced will depend in large part on the quality of the elements used for concatenation. Selection and evaluation of these elements has been done entirely by hand. The proposed work addresses the process by which these concatenative elements are created from a natural voice and optimized. The optimization uses distance measures which exploit detailed information on the structure of the speech signals

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Robust Automatic Transcription of Lectures

Die automatische Transkription von Vorträgen, Vorlesungen und Präsentationen wird immer wichtiger und ermöglicht erst die Anwendungen der automatischen Übersetzung von Sprache, der automatischen Zusammenfassung von Sprache, der gezielten Informationssuche in Audiodaten und somit die leichtere Zugänglichkeit in digitalen Bibliotheken. Im Idealfall arbeitet ein solches System mit einem Mikrofon das den Vortragenden vom Tragen eines Mikrofons befreit was der Fokus dieser Arbeit ist

Speech Recognition in noisy environment using Deep Learning Neural Network

Recent researches in the field of automatic speaker recognition have shown that methods based on deep learning neural networks provide better performance than other statistical classifiers. On the other hand, these methods usually require adjustment of a significant number of parameters. The goal of this thesis is to show that selecting appropriate value of parameters can significantly improve speaker recognition performance of methods based on deep learning neural networks. The reported study introduces an approach to automatic speaker recognition based on deep neural networks and the stochastic gradient descent algorithm. It particularly focuses on three parameters of the stochastic gradient descent algorithm: the learning rate, and the hidden and input layer dropout rates. Additional attention was devoted to the research question of speaker recognition under noisy conditions. Thus, two experiments were conducted in the scope of this thesis. The first experiment was intended to demonstrate that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance under no presence of noise. This experiment was conducted in two phases. In the first phase, the recognition rate is observed when the hidden layer dropout rate and the learning rate are varied, while the input layer dropout rate was constant. In the second phase of this experiment, the recognition rate is observed when the input layers dropout rate and learning rate are varied, while the hidden layer dropout rate was constant. The second experiment was intended to show that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance even under noisy conditions. Thus, different noise levels were artificially applied on the original speech signal