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This work presents Trial-Based Calibration (TBC), a novel, automated calibration technique robust to both unseen and widely varying conditions.

Jun, 2014
In Proceedings
420

This paper proposes two novel frontends for robust language identification (LID) using a convolutional neural network (CNN) trained for automatic speech recognition (ASR).

Jun, 2014
In Proceedings
420
By Julien van Hout, Dimitra Vergyri, Vikramjit Mitra

State-of-the-art calibration and fusion approaches for spoken term detection (STD) systems currently rely on a multi-pass approach where the scores are calibrated, then fused, and finally re-calibrated to obtain a single decision threshold across keywords.

May, 2014
In Proceedings
420
By Elizabeth Shriberg

Recent studies have shown the importance of using online videos along with textual material in educational instruction, especially for better content retention and improved concept understanding. A key question is how to select videos to maximize student engagement, particularly when there are...

May, 2014
In Proceedings
420

This paper assesses the role of robust acoustic features in spoken term detection (a.k.a keyword spotting—KWS) under heavily degraded channel and noise corrupted conditions.

May, 2014
In Proceedings
420

We address the problem of subselecting a large set of acoustic data to train automatic speech recognition (ASR) systems.

May, 2014
In Proceedings
420

We present a system for detecting lexical stress in English words spoken by English learners.

May, 2014
In Proceedings
420

Recently, a new version of the iVector modelling has been proposed for noise robust speaker recognition, where the nonlinear function that relates clean and noisy cepstral coefficients is approximated by a first order vector Taylor series (VTS). In this paper, it is proposed to substitute the first...

May, 2014
In Proceedings
420
By Yik-Cheung Tam, Yun Lei, Jing Zheng, Wen Wang

Detecting automatic speech recognition (ASR) errors can play an important role for effective human-computer spoken dialogue system, as recognition errors can hinder accurate system understanding of user intents.

May, 2014
In Proceedings
420

We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR).

May, 2014
In Proceedings
420

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