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

Jul, 2014
In Book
412

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

Jun, 2014
In Proceedings
412

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
412

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
412

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

May, 2014
In Proceedings
412

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

May, 2014
In Proceedings
412

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
412
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
412

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
412

The authors propose a novel staged hybrid model for emotion detection in speech.

May, 2014
In Proceedings
412

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