On Breathing Pattern Information in Synthetic Speech
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
In hidden Markov model (HMM) based automatic speech recognition (ASR) system, modeling the statistical relationship between the acoustic speech signal and the HMM states that represent linguistically motivated subword units such as phonemes is a crucial st ...
Language independent query-by-example spoken term detection (QbE-STD) is the problem of retrieving audio documents from an archive, which contain a spoken query provided by a user. This is usually casted as a hypothesis testing and pattern matching problem ...
Speech intelligibility is an important assessment criterion of the communicative performance of pathological speakers. To assist clinicians in their assessment, time- and cost-efficient automatic intelligibility measures offering a repeatable and reliable ...
The goal of this thesis is to improve current state-of-the-art techniques in speaker verification
(SV), typically based on âidentity-vectorsâ (i-vectors) and deep neural network (DNN), by exploiting diverse (phonetic) information extracted using variou ...
In a recent work, we have shown that speaker verification systems can be built where both features and classifiers are directly learned from the raw speech signal with convolutional neural networks (CNNs). In this framework, the training phase also decides ...
Recognising dysarthric speech is a challenging problem as it differs in many aspects from typical speech, such as speaking rate and pronunciation. In the literature the focus so far has largely been on handling these variabilities in the framework of HMM/G ...
Modeling directly raw waveforms through neural networks for speech processing is gaining more and more attention. Despite its varied success, a question that remains is: what kind of information are such neural networks capturing or learning for different ...
We show that confidence measures estimated from local posterior probabilities can serve as objective functions for training ANNs in hybrid HMM based speech recognition systems. This leads to a segment-level training paradigm that overcomes the limitation o ...
Modeling directly raw waveform through neural networks for speech processing is gaining more and more attention. Despite its varied success, a question that remains is: what kind of information are such neural networks capturing or learning for different t ...
A new software for modeling pathological speech signals is presented in this paper. The software is called NeuroSpeech. This software enables the analysis of pathological speech signals considering different speech dimensions: phonation, articulation, pros ...
