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Hynek Hermansky

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Publications associées (114)

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In this paper, we investigate the significance of contextual information in a phoneme recognition system using the hidden Markov model - artificial neural network paradigm. Contextual information is probed at the feature level as well as at the output of t ...

2008

Combining Evidence from a Generative and a Discriminative Model in Phoneme Recognition

Hynek Hermansky, Joel Praveen Pinto

We investigate the use of the log-likelihood of the features obtained from a generative Gaussian mixture model, and the posterior probability of phonemes from a discriminative multilayered perceptron in multi-stream combination for recognition of phonemes. ...

2008

Front-end for Far-field Speech Recognition based on Frequency Domain Linear Prediction

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

Automatic Speech Recognition (ASR) systems usually fail when they encounter speech from far-field microphone in reverberant environments. This is due to the application of short-term feature extraction techniques which do not compensate for the artifacts i ...

2008

Unexpected stimuli are a challenge to any machine learning algorithm. Here we identify distinct types of unexpected events, focusing on ’incongruent events’ - when ’general level’ and ’specific level’ classifiers give conflicting predictions. We define a f ...

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

Combining Evidence from a Generative and a Discriminative Model in Phoneme Recognition

Hynek Hermansky, Joel Praveen Pinto

IDIAP2008

Front-end for Far-field Speech Recognition based on Frequency Domain Linear Prediction

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

IDIAP2008

This paper proposes modifications to the Multi-resolution RASTA (MRASTA) feature extraction technique for the automatic speech recognition (ASR). By emulating asymmetries of the temporal receptive field (TRF) profiles of auditory mid-brain neurons, we obta ...

2008

We propose a new auditory inspired feature extraction technique for automatic speech recognition (ASR). Features are extracted by filtering the temporal trajectory of spectral energies in each critical band of speech by a bank of finite impulse response (F ...

2008

Spectro-Temporal Features for Automatic Speech Recognition using Linear Prediction in Spectral Domain

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

Frequency Domain Linear Prediction (FDLP) provides an efficient way to represent temporal envelopes of a signal using auto-regressive models. For the input speech signal, we use FDLP to estimate temporal trajectories of sub-band energy by applying linear p ...

2008