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MULTITASK LEARNING TO IMPROVE ARTICULATORY FEATURE ESTIMATION AND PHONEME RECOGNITION

Related publications (46)

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Fast Approximate Spoken Term Detection from Sequence of Phonemes

Hynek Hermansky, Joel Praveen Pinto

We investigate the detection of spoken terms in conversational speech using phoneme recognition with the objective of achieving smaller index size as well as faster search speed. Speech is processed and indexed as a sequence of one best phoneme sequence. W ...

IDIAP2008

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

MLP-based Log Spectral Energy Mapping for Robust Overlapping Speech Recognition

Hervé Bourlard, John David Scott Dines, Weifeng Li

This paper investigates a multilayer perceptron (MLP) based acoustic feature mapping to extract robust features for automatic speech recognition (ASR) of overlapping speech. The MLP is trained to learn the mapping from log mel filter bank energies (MFBEs) ...

IDIAP2007

Truncation Confusion Patterns in Onset Consonants

Confusion matrices and truncation experiments have long been a part of psychoacoustic experimentation. However confusion matrices are seldom used to analyze truncation experiments. A truncation experiment was conducted and the confusion patterns were analy ...

IDIAP2007

Using auxiliary sources of knowledge for automatic speech recognition

Mathew Magimai Doss

Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variatio ...

EPFL2005

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

École Polytechnique Fédérale de Lausanne, Computer Science Department2005

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

IDIAP2005

Robust audio segmentation

Jitendra Ajmera

Audio segmentation, in general, is the task of segmenting a continuous audio stream in terms of acoustically homogenous regions, where the rule of homogeneity depends on the task. This thesis aims at developing and investigating efficient, robust and unsup ...

EPFL2005

A Discriminative Decoder for the Recognition of Phoneme Sequences

Samy Bengio, David Grangier

In this report, we propose a discriminative decoder for phoneme recognition, i.e. the identification of the uttered phoneme sequence from a speech recording. This task is solved as a 3 step process: a phoneme classifier first classifies each accoustic fram ...

IDIAP2005