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The introduction of deep neural networks has significantly improved automatic speech recognition performance.

Sep, 2016
In Proceedings
Topics:
413

This work investigates whether nonlexical information from speech can automatically predict the quality of small-group collaborations.

Sep, 2016
Technical Report
413

Often, prior knowledge of subword units is unavailable for low-resource languages. Instead, a global subword unit description, such as a universal phone set, is typically used in such scenarios.

Sep, 2016
In Proceedings
Topics:
413
By Wen Wang, Haibo Li, Heng Ji

We propose approaches improving statistical machine translation (SMT) performance, by developing name-aware language model adaptations and sparse features, in addition to extracting nameaware translation grammar and rules, adding name phrase table, and name translation driven decoding.

Dec, 2015
In Proceedings
413
By Takaaki Hori, Zhuo Chen, Hakan Erdogan, John R. Hershey, Jonathan Le Roux, Vikramjit Mitra, Shinji Watanabe

This paper introduces the MERL/SRI system designed for the 3rd CHiME speech separation and recognition challenge (CHiME-3).

Dec, 2015
In Proceedings
413

Convolutional deep neural networks (CDNNs) have consistently shown more robustness to noise and background contamination than traditional deep neural networks (DNNs).

Dec, 2015
In Proceedings
413
By Luciana Ferrer, Yun Lei, Mitchell McLaren, Nicolas Scheffer

This paper compares different approaches for using deep neural networks (DNNs) trained to predict senone posteriors for the task of spoken language recognition (SLR).

Oct, 2015
Article
413

There is a critical need for detection and monitoring of Post-Traumatic Stress Disorder (PTSD) in both military and civilian populations.

Sep, 2015
In Proceedings
413

We introduce a new dataset for the study of the effect of highly non-stationary noises on language recognition (LR) performance.

Sep, 2015
In Proceedings
413

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