Author: Aaron Lawson
-
Detection of Demographics and Identity in Spontaneous Speech and Writing
This chapter focuses on the automatic identification of demographic traits and identity in both speech and writing.
-
Identifying User Demographic Traits through Virtual-World Language Use
The paper presents approaches for identifying real-world demographic attributes based on language use in the virtual world.
-
Application of Convolutional Neural Networks to Language Identification in Noisy Conditions
This paper proposes two novel frontends for robust language identification (LID) using a convolutional neural network (CNN) trained for automatic speech recognition (ASR).
-
Trial-Based Calibration for Speaker Recognition in Unseen Conditions
This work presents Trial-Based Calibration (TBC), a novel, automated calibration technique robust to both unseen and widely varying conditions.
-
Recent Developments in Voice Biometrics: Robustness and High Accuracy
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.
-
Improving Language Identification Robustness to Highly Channel-Degraded Speech through Multiple System Fusion
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.
-
Adaptive Gaussian Backend for Robust Language Identification
This paper proposes adaptive Gaussian backend (AGB), a novel approach to robust language identification (LID).
-
Promoting robustness for speaker modeling in the community: the PRISM evaluation set
We introduce a new database for evaluation of speaker recognition systems.