Speech & natural language publications

We recently proposed the use of deep neural networks (DNN) in place of Gaussian Mixture models (GMM) in the i-vector extraction process for speaker recognition. 

This paper applies a convolutional neural network (CNN) trained for automatic speech recognition (ASR) to the task of speaker identification (SID).

In this work we present a study exploring both conventional DNNs and deep Convolutional Neural Networks (CNN) for noise- and channel-degraded speech recognition tasks using the Aurora4 dataset. 

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 explores in depth a recently proposed approach to spoken language recognition based on the estimated posteriors for a set of senones representing the phonetic space of one or…

We show how content matching can be effectively done at the statistics level to enable the use of standard verification backends. While no significant improvements were observed for the general…

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

This paper presents a deep neural network (DNN) to extract articulatory information from the speech signal and explores different ways to use such information in a continuous speech recognition task.

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 investigate techniques for boosting the accuracy of automatic phonetic segmentation based on HMM acoustic-phonetic models.