no code implementations • 29 Aug 2017 • Pranay Dighe, Afsaneh Asaei, Hervé Bourlard
We propose an information theoretic framework for quantitative assessment of acoustic modeling for hidden Markov model (HMM) based automatic speech recognition (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 18 Oct 2016 • Pranay Dighe, Afsaneh Asaei, Herve Bourlard
Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training.
no code implementations • 15 Apr 2016 • Milos Cernak, Alexandros Lazaridis, Afsaneh Asaei, Philip N. Garner
Segmental errors are further propagated to optional suprasegmental (such as syllable) information coding.
no code implementations • 22 Jan 2016 • Pranay Dighe, Gil Luyet, Afsaneh Asaei, Herve Bourlard
We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low-dimensional subspaces.
no code implementations • 21 Jan 2016 • Milos Cernak, Afsaneh Asaei, Hervé Bourlard
Building on findings from converging linguistic evidence on the gestural model of Articulatory Phonology as well as the neural basis of speech perception, we hypothesize that phonological posteriors convey properties of linguistic classes at multiple time scales, and this information is embedded in their support (index) of active coefficients.
no code implementations • 31 Aug 2014 • Mohammad J. Taghizadeh, Reza Parhizkar, Philip N. Garner, Herve Bourlard, Afsaneh Asaei
This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available.