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By Mitchell McLaren, Martin Graciarena, Vikramjit Mitra

The National Institute of Standards and Technology (NIST) 2012 speaker recognition evaluation posed several new challenges including noisy data, varying test-sample length and number of enrollment samples, and a new metric.

Aug, 2013
In Proceedings
418
By Martin Graciarena, Horacio Franco, Vikramjit Mitra

Speech activity detection (SAD) on channel transmissions is a critical preprocessing task for speech, speaker and language recognition or for further human analysis.

Aug, 2013
In Proceedings
418

We describe a language identification system developed for robustess to noise conditions such as those encountered under the DARPA RATS program, which is focused on multi-channel audio collected in high noise conditions.

Aug, 2013
In Proceedings
418
By Vikramjit Mitra, Horacio Franco, Martin Graciarena

This paper presents a new signal-processing technique motivated by the physiology of human auditory system. In this approach, auditory hair cells are modeled as damped oscillators that are stimulated by bandlimited time domain speech signals acting as forcing functions.

Aug, 2013
In Proceedings
418

Current state-of-the-art speaker identification (SID) systems perform exceptionally well under clean conditions, but their performance deteriorates when noise and channel degradations are introduced.

Aug, 2013
In Proceedings
418
By Vikramjit Mitra, Wen Wang

This study attempts to improve automatic phonetic segmentation within the HMM framework.

Aug, 2013
In Proceedings
418

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.

Aug, 2013
In Proceedings
418

This paper proposes adaptive Gaussian backend (AGB), a novel approach to robust language identification (LID).

Aug, 2013
In Proceedings
418

We propose a novel approach for noise-robust speaker recognition, where the model of distortions caused by additive and convolutive noises is integrated into the i-vector extraction framework.

May, 2013
In Proceedings
418
By Vikramjit Mitra

Improving the robustness of speech recognition systems to cope with adverse background noise is a challenging research topic.

May, 2013
In Proceedings
418

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