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Recognizing speech under high levels of channel and/or noise degradation is challenging.

Sep, 2016
In Proceedings
Topics:
418

The newly collected Speakers in the Wild (SITW) database was central to a text-independent speaker recognition challenge held as part of a special session at Interspeech 2016. The SITW database is composed of audio recordings from 299 speakers collected from open source media, with an average of 8...

Sep, 2016
In Proceedings
Topics:
418

This article is concerned with the issue of calibration in the context of Deep Neural Network (DNN) based approaches to speaker recognition. DNNs have provided a new standard in technology when used in place of the traditional universal background model (UBM) for feature alignment, or to augment...

Sep, 2016
In Proceedings
Topics:
418
By Vikramjit Mitra, Horacio Franco

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

Sep, 2016
In Proceedings
Topics:
418

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

Sep, 2016
Technical Report
418
By Vikramjit Mitra, Dimitra Vergyri, Horacio Franco

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:
418

Annotating audio data for the presence and location of speech is a time-consuming and therefore costly task.

Sep, 2016
In Proceedings
Topics:
418

The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology on single and multi-speaker audio acquired across unconstrained or “wild” conditions. The...

Sep, 2016
In Proceedings
Topics:
418

We present the work done by our group for the 2015 language recognition evaluation (LRE) organized by the National Institute of Standards and Technology (NIST).

Jun, 2016
In Proceedings
418
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
418

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