Author: Horacio Franco

In this work, we present robust acoustic features motivated by human speech perception for use in a convolutional deep neural network-based acoustic model for recognizing continuous speech in a reverberant condition.

In this work we present a study exploring both conventional DNNs and deep Convolutional Neural Networks (CNN) for noise- and channel-degraded speech recognition tasks using the Aurora4 dataset. 

We present improvements to a keyword spotting (KWS) system that operates in highly adverse channel conditions with very low signal-to-noise ratio levels. 

We present a system for detecting lexical stress in English words spoken by English learners.  The system uses both spectral and segmental features to detect three levels of stress for each syllable in a word. 

This paper assesses the role of robust acoustic features in spoken term detection (a.k.a keyword spotting—KWS) under heavily degraded channel and noise corrupted conditions. 

In the context of computer-aided language learning, automatic detection of specific phone mispronunciations by nonnative speakers can be used to provide detailed feedback about specific pronunciation problems.

We present design strategies for a keyword spotting (KWS) system that operates in highly degraded channel conditions with very low signal-to-noise ratio levels.

In this paper, we present a robust acoustic feature on top of robust modeling techniques to further improve speaker identification performance.

This paper presents a new signal-processing technique motivated by the physiology of human auditory system.

In this work, we present an amplitude modulation feature derived from Teager’s nonlinear energy operator that is power normalized and cosine transformed to produce normalized modulation cepstral coefficient (NMCC) features…