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Publications associées (25)

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

Perceptual Information Loss due to Impaired Speech Production

Hervé Bourlard, Milos Cernak, Afsaneh Asaei

Phonological classes define articulatory-free and articulatory-bound phone attributes. Deep neural network is used to estimate the probability of phonological classes from the speech signal. In theory, a unique combination of phone attributes form a phonem ...

2017

Spatial hearing rendering in wireless microphone systems for binaural hearing aids

Gilles André Courtois

In 2015, 360 million people, including 32 million children, were suffering from hearing impairment all over the world. This makes hearing disability a major worldwide issue. In the US, the prevalence of hearing loss increased by 160% over the past generati ...

EPFL2016

Probabilistic Lexical Modeling and Unsupervised Training for Zero-Resourced ASR

Ramya Rasipuram, Marzieh Razavi

Standard automatic speech recognition (ASR) systems rely on transcribed speech, language models, and pronunciation dictionaries to achieve state-of-the-art performance. The unavailability of these resources constrains the ASR technology to be available for ...

2013

Automatic Speech Indexing System of Bilingual Video Parliament Interventions

Petr Motlicek, Philip Neil Garner, Milos Cernak

This paper presents the development and evaluation of an automatic audio indexing system designed for a special task: work in a bilingual environment in the Parliament of the Canton of Valais in Switzerland, with two official languages, German and French. ...

Idiap2013

Improving Articulatory Feature and Phoneme Recognition using Multitask Learning

Ramya Rasipuram

Speech sounds can be characterized by articulatory features. Articulatory features are typically estimated using a set of multilayer perceptrons (MLPs), i.e., a separate MLP is trained for each articulatory feature. In this paper, we investigate multitask ...

Springer Berlin / Heidelberg2011

Enhancing posterior based speech recognition systems

Hamed Ketabdar

The use of local phoneme posterior probabilities has been increasingly explored for improving speech recognition systems. Hybrid hidden Markov model / artificial neural network (HMM/ANN) and Tandem are the most successful examples of such systems. In this ...

EPFL2008

Enhancing posterior based speech recognition systems

Hamed Ketabdar

Ecole Polytechnique Fédérale de Lausanne2008

Modulation Frequency Features For Phoneme Recognition In Noisy Speech

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

In this letter, a new feature extraction technique based on modulation spectrum derived from syllable-length segments of sub-band temporal envelopes is proposed. These sub-band envelopes are derived from auto-regressive modelling of Hilbert envelopes of th ...

2008

Modulation Frequency Features For Phoneme Recognition In Noisy Speech

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

In this paper, a new feature extraction technique based on modulation spectrum derived from syllable-length segments of sub-band temporal envelopes is proposed. These sub-band envelopes are derived from auto-regressive modelling of Hilbert envelopes of the ...

Idiap2008

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