Author: Martin Graciarena

This work addresses the problem of speaker verification where additive noise is present in the enrollment and testing utterances.

We introduce a new database for evaluation of speaker recognition systems. 

The goal of this work was to explore modeling techniques to improve bird species classification from audio samples.

This paper presents the CALO-MA architecture and its speech recognition and understanding components.

The goal of this work was to explore the optimization of the feature extraction module (front-end) parameters to improve bird species recognition.

In this paper we explore the use of other speaker verification systems on the telephone channel data and compare against the GMM baseline. We found the GMM system to be one of the more robust across all conditions.

We describe the large vocabulary automatic speech recognition system developed for Modern Standard Arabic used for the 2007 GALE evaluation as part of the speech translation system.

We study the question of how intrinsic variations (associated with the speaker rather than the recording environment) affect text-independent speaker verification performance.

We propose a modified linear logistic regression procedure that conditions combination weights on the auxiliary information. A regularization procedure is used to control the complexity of the extended model.

We report on machine learning experiments to distinguish deceptive from nondeceptive speech in the Columbia-SRI-Colorado (CSC) corpus. Specifically, we propose a system combination approach using different models and features for deception detection.

We extend the POF algorithm to allow a more accurate way to select noisy-to-clean feature mappings, by allowing different combinations of speech and noise to have combination-specific mappings selected depending on the observation.

We present results from a study seeking to distinguish deceptive from non-deceptive speech using machine learning techniques on features extracted from a large corpus of deceptive and non-deceptive speech. We present current results comparing the performance of acoustic/ prosodic, lexical, and speaker-dependent features and discuss future research directions.