Claudio Vair

Claudio Vair
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Main area of research
Biometrics

Claudio Vair is a Senior Researcher on the Voice Biometrics team. His research interest is in the area of biometrics. Before coming to Nuance, Claudio worked as a research scientist at CSELT (Centro Studi e Laboratori Telecomunicazioni) on Hidden Markov Model training and adaptation, speech signal processing, and language modeling. After CSELT, he joined the speech technology department at Loquendo in Torino, Italy. Claudio was a key contributor in the development of Loquendo Automatic Speech Recognition (ASR) and the Loquendo Voice Security Library (LVSL). The latter has been administered in several NIST speaker and language recognition evaluations, obtaining certified world-class results. Claudio received an M.S. in Computer Engineering and a Master in Telecommunications degree from Politecnico di Torino, Italy. Claudio has also published 30 papers in conferences and journals, along with eight issued patents. His current interests are in the fields of voice biometrics, speaker and language recognition, neural networks and machine learning.

Selected articles

Nuance – Politecnico di Torino’s 2012 NIST speaker recognition evaluation system

This paper describes the Nuance-Politecnico di Torino (NPT) speaker recognition system submitted to the NIST SRE12 evaluation campaign. Included are the results of post-evaluation tests,

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Loquendo-Politecnico Di Torino system for the 2009 NIST language recognition evaluation

This paper describes the system submitted by Loquendo and Politecnico di Torino (LPT) for the 2009 NIST Language Recognition Evaluation. The system is a combination

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Compensation of nuisance factors for speaker and language recognition

The variability of the channel and environment is one of the most important factors affecting the performance of text-independent speaker verification systems. The best techniques

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Stream-based speaker segmentation using speaker factors and eigenvoices

This paper presents a stream-based approach for unsupervised multi-speaker conversational speech segmentation. The main idea of this work is to exploit prior knowledge about the

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