Publications de Hervé Bourlard

Autoencoders reloaded

In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called "auto-associative multilayer perceptrons") were, in the best case, implementing singular value decomposit ...

SPRINGER2022

Comparison of 5 methods for the evaluation of intelligibility in mild to moderate French dysarthric speech

Hervé Bourlard

Altered quality of the phonetic-acoustic information in the speech signal in the case of motor speech disorders may reduce its intelligibility. Monitoring intelligibility is part of the standard clinical assessment of patients. It is also a valuable tool t ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Hervé Bourlard, Selen Hande Kabil

Starting from a strong Lattice-Free Maximum Mutual Information (LF-MMI) baseline system, we explore different autoencoder configurations to enhance Mel-Frequency Cepstral Coefficients (MFCC) features. Autoencoders are expected to generate new MFCC features ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Multitask adaptation with Lattice-Free MMI for multi-genre speech recognition of low resource languages

Hervé Bourlard, Petr Motlicek

In this paper, we develop Automatic Speech Recognition (ASR) systems for multi-genre speech recognition of low-resource languages where training data is predominantly conversational speech but test data can be in one of the following genres: news broadcast ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Lattice-Free Mmi Adaptation Of Self-Supervised Pretrained Acoustic Models

Hervé Bourlard, Apoorv Vyas

In this work, we propose lattice-free MMI (LFMMI) for supervised adaptation of self-supervised pretrained acoustic model. We pretrain a Transformer model on thousand hours of untranscribed Librispeech data followed by supervised adaptation with LFMMI on th ...

IEEE2021

Automatic and perceptual discrimination between dysarthria, apraxia of speech, and neurotypical speech

Hervé Bourlard, Ina Kodrasi

IEEE Signal Processing Society2021

Subspace-Based Learning for Automatic Dysarthric Speech Detection

Hervé Bourlard, Parvaneh Janbakhshi, Ina Kodrasi

To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated ...

IEEE Institute of Electrical and Electronics Engineers2021

Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model

Hervé Bourlard, Apoorv Vyas

In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the overfitting issues with connectionist temporal classification (CTC) training to reduce its performance gap with flat-start lattice-free MMI (E2E-LFMMI) for autom ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Automatic Dysarthric Speech Detection Exploiting Pairwise Distance-Based Convolutional Neural Networks

Hervé Bourlard, Parvaneh Janbakhshi, Ina Kodrasi

Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we propose a novel automatic dysarthric speech detection approach based on analy ...

IEEE2021

Synthetic Speech References for Automatic Pathological Speech Intelligibility Assessment

Hervé Bourlard, Parvaneh Janbakhshi, Ina Kodrasi

Automatic pathological speech intelligibility measures are crucial to assist the clinical diagnosis and treatment of speech disorders. The recently proposed pathological short-time objective intelligibility (P-ESTOI) measure was shown to be very advantageo ...

IEEE2020

Incremental Semi-Supervised Learning for Multi-Genre Speech Recognition

Hervé Bourlard, Petr Motlicek, Subhadeep Dey

2020

Automatic Pathological Speech Intelligibility Assessment Exploiting Subspace-Based Analyses

Hervé Bourlard, Parvaneh Janbakhshi, Ina Kodrasi

Competitive state-of-the-art automatic pathological speech intelligibility measures typically rely on regression training on a large number of features, require a large amount of healthy speech training data, or are applicable only to phonetically balanced ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Spectro-temporal sparsity characterization for dysarthric speech detection

Hervé Bourlard, Ina Kodrasi

IEEE2020

Neural Network Based End-to-End Query by Example Spoken Term Detection

Hervé Bourlard, Dhananjay Ram

This article focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State-of-the-art approaches primarily rely on dynamic time warping (DTW) based template matching techniques using phone posterior or bottlene ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Lattice-Free Maximum Mutual Information Training of Multilingual Speech Recognition System

Hervé Bourlard, Petr Motlicek, Sibo Tong

ISCA2020

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional e ...

ELSEVIER2020

Automatic Discrimination of Apraxia of Speech and Dysarthria using a Minimalistic Set of Handcrafted Features

Hervé Bourlard, Ina Kodrasi

ISCA2020

An End-to-End Networks to Synthetize Intonation using a Generalized Command Response Model

Hervé Bourlard, Philip Neil Garner, Bastian Schnell, François Thierry M Marelli

The generalized command response (GCR) model represents intonation as a superposition of muscle responses to spike command signals. We have previously shown that the spikes can be predicted by a two-stage system, consisting of a recurrent neural network an ...

Idiap2019

Low-rank and sparse subspace modeling of speech for DNN based acoustic modeling

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

Towards the goal of improving acoustic modeling for automatic speech recognition (ASR), this work investigates the modeling of senone subspaces in deep neural network (DNN) posteriors using low-rank and sparse modeling approaches. While DNN posteriors are ...

ELSEVIER SCIENCE BV2019

Multilingual Bottleneck Features For Query By Example Spoken Term Detection

Hervé Bourlard, Dhananjay Ram

State of the art solutions to query by example spoken term detection (QbE-STD) rely on bottleneck feature representation of the query and audio document. Here, we present a study on QbE-STD performance using several monolingual as well as multilingual bott ...

IEEE2019