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

Publications associées (49)

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Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Mapping Bibliotheca Hertziana

Hannah Laureen Casey

The project introduces an innovative visual method for analysing libraries and archives, with a focus on Bibliotheca Hertziana’s library collection. This collection, which dates back over a century, is examined by integrating user loan data with deep mappi ...

2024

Sparse Autoencoders for Speech Modeling and Recognition

Selen Hande Kabil

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

EPFL2023

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Alexis Berne, Gionata Ghiggi

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

COPERNICUS GESELLSCHAFT MBH2023

Autoencoders reloaded

Hervé Bourlard, Selen Hande Kabil

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

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

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

Multilingual Training and Adaptation in Speech Recognition

Sibo Tong

State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ...

EPFL2020

Dynamic Model Pruning with Feedback

Martin Jaggi, Sebastian Urban Stich, Luis Felipe Barba Flores, Tao Lin, Daniil Dmitriev

Deep neural networks often have millions of parameters. This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference. We propose a novel model compression method that gener ...

2020

A deep learning framework for matching of SAR and optical imagery

Devis Tuia, Sylvain Lobry, Lloyd Haydn Hughes

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. Wh ...

2020