Martin Graciarena

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

This paper presents SRI’s submission along with a careful analysis of the approaches that provided gains for this challenging evaluation including a multiclass voice-activity detection system, the use of noisy data in system training, and the fusion of subsystems using acoustic characterization metadata.

This article describes our submission to the speaker identification (SID) evaluation for the first phase of the DARPA Robust Audio and Transcription of Speech (RATS) program. 

We evaluate the effect that improved audio quality has for speaker verification performance, using a recently released full-bandwidth version of microphone data from the SRE2010 evaluation.

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…

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.