Author: Aaron Lawson

This chapter focuses on the automatic identification of demographic traits and identity in both speech and writing.

The paper presents approaches for identifying real-world demographic attributes based on language use in the virtual world.

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

This work presents Trial-Based Calibration (TBC), a novel, automated calibration technique robust to both unseen and widely varying conditions.

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.

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.

We introduce a new database for evaluation of speaker recognition systems.