Author: Mitchell McLaren

This paper proposes two novel frontends for robust language identification (LID) using a convolutional neural network (CNN) trained for automatic speech recognition (ASR).

We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR).

This paper investigates the effect of utterance duration to the calibration of a modern i-vector speaker recognition system with probabilistic linear discriminant analysis (PLDA) modeling.

We highlight SRI’s innovations that resulted from the IARPA Biometrics Exploitation Science & Technology (BEST) and the DARPA Robust Automatic Transcription of Speech (RATS) programs, as well as SRI’s approach for codec degraded speech.

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

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 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.