Author: Dimitra Vergyri

This study presents an efficient and effective score calibration technique for keyword detection that is based on the logistic regression calibration approach commonly used in forensic speaker identification. 

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. 

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.

This work proposes the use of discriminative training to construct a phoneme confusion model, which expands the phonemic index of a KWS system by adding phonemic variation to handle the abovementioned problems.

In this study, we focus on speech features that can identify the speaker’s emotional health, i.e., whether the speaker is depressed or not. 

We study the effectiveness of recently developed language recognition techniques based on speech recognition models for the discrimination of Arabic dialects.

In this work, we compare several known approaches for multilingual acoustic modeling for three languages, Dari, Farsi and Pashto, which are of recent geo-political interest.

We investigate the effect of multiple accents on an English broadcast news recognition system.

In this paper, we propose a novel question answering system that searches for responses from spoken documents such as broadcast news stories and conversations.