Speech & natural language publications

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…

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

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.

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…

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

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

We address the problem of retrieving spoken information from noisy and heterogeneous audio archives using a rich system combination with a diverse set of noise-robust modules and audio characterization.

We present a neural network model to estimate articulatory trajectories from speech signals where the model was trained using synthetic speech signals generated by Haskins Laboratories’ task-dynamic model of speech…

We propose a novel approach for noise-robust speaker recognition, where the model of distortions caused by additive and convolutive noises is integrated into the i-vector extraction framework.

This paper shows that fusion of multiple noise robust feature streams motivated by speech production and perception theories help to significantly improve the robustness of traditional speech recognition systems.