Publications associées à Discours de haine

Can Self-Supervised Neural Networks Pre-Trained on Human Speech distinguish Animal Callers?

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...

ISCA2023

On Breathing Pattern Information in Synthetic Speech

Mathew Magimai Doss, Zohreh Mostaani

The respiratory system is an integral part of human speech production. As a consequence, there is a close relation between respiration and speech signal, and the produced speech signal carries breathing pattern related information. Speech can also be gener ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

Subrahmanya Pavankumar Dubagunta

Speech signal conveys several kinds of information such as a message, speaker identity, emotional state of the speaker and social state of the speaker. Automatic speech assessment is a broad area that refers to using automatic methods to predict human judg ...

EPFL2021

Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Julian David Fritsch, Subrahmanya Pavankumar Dubagunta

Speech-based degree of sleepiness estimation is an emerging research problem. This paper investigates an end-to-end approach, where given raw waveform as input, a convolutional neural network (CNN) estimates at its output the degree of sleepiness. Within t ...

2020

Intonation Modelling for Speech Synthesis and Emphasis Preservation

Pierre-Edouard Jean Charles Honnet

Speech-to-speech translation is a framework which recognises speech in an input language, translates it to a target language and synthesises speech in this target language. In such a system, variations in the speech signal which are inherent to natural hum ...

EPFL2017

Incremental Syllable-Context Phonetic Vocoding

Petr Motlicek, Philip Neil Garner, Milos Cernak

Current very low bit rate speech coders are, due to complexity limitations, designed to work off-line. This paper investigates incremental speech coding that operates real-time and incrementally (i.e., encoded speech depends only on already-uttered speech ...

Idiap2015

Incremental Syllable-Context Phonetic Vocoding

Petr Motlicek, Philip Neil Garner, Milos Cernak

2015

Who Wants To Be A Millionaire? (II)

Hervé Bourlard, Petr Motlicek, Huseyn Gasimov

The developed and implemented game exploiting state-of-the-art speech processing technologies is called “Who wants to be a millionaire?”. This game simulates well-known TV-based quiz game spread in many countries. This optional project, partially made at E ...

Idiap2013

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Discours de haine

Novel Methods For Detection And Analysis Of Atypical Aspects In Speech

Can Self-Supervised Neural Networks Pre-Trained on Human Speech distinguish Animal Callers?

On Breathing Pattern Information in Synthetic Speech

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Intonation Modelling for Speech Synthesis and Emphasis Preservation

"Can you hear me now?"

Incremental Syllable-Context Phonetic Vocoding

Incremental Syllable-Context Phonetic Vocoding

Who Wants To Be A Millionaire? (II)

Graph Chatbot

Chattez avec Graph Search

Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Can Self-Supervised Neural Networks Pre-Trained on Human Speech distinguish Animal Callers?

Novel Methods For Detection And Analysis Of Atypical Aspects In Speech

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

On Breathing Pattern Information in Synthetic Speech

Incremental Syllable-Context Phonetic Vocoding

Who Wants To Be A Millionaire? (II)

Intonation Modelling for Speech Synthesis and Emphasis Preservation

Incremental Syllable-Context Phonetic Vocoding

"Can you hear me now?"