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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

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

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

May, 2014
In Proceedings
420

In the context of computer-aided language learning, automatic detection of specific phone mispronunciations by nonnative speakers can be used to provide detailed feedback about specific pronunciation problems.

May, 2014
In Proceedings
Topics:
420

Studies have shown that the performance of state-of-the-art automatic speech recognition (ASR) systems significantly deteriorate with increased noise levels and channel degradations, when compared to human speech recognition capability. Traditionally, noise-robust acoustic features are deployed to...

May, 2014
In Proceedings
420
By Elizabeth Shriberg

Current speech-input systems typically use a nonspeech threshold for end-of-utterance detection. While usually sufficient for short utterances, the approach can cut speakers off during pauses in more complex utterances. We elicit personal-assistant speech (reminders, calendar entries, messaging,...

May, 2014
In Proceedings
420

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