Author: Martin Graciarena

We present improvements to a keyword spotting (KWS) system that operates in highly adverse channel conditions with very low signal-to-noise ratio levels. 

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.

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.

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.

This paper presents a new signal-processing technique motivated by the physiology of human auditory system.

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…