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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
By Vikramjit Mitra, Wen Wang, Andreas Kathol

Reverberation in speech degrades the performance of speech recognition systems, leading to higher word error rates.

May, 2014
In Proceedings
420
By Vikramjit Mitra

This paper presents a deep neural network (DNN) to extract articulatory information from the speech signal and explores different ways to use such information in a continuous speech recognition task.

May, 2014
In Proceedings
420
By Vikramjit Mitra, Wen Wang

Accurate phone-level segmentation of speech remains an important task for many subfields of speech research. We investigate techniques for boosting the accuracy of automatic phonetic segmentation based on HMM acoustic-phonetic models.

May, 2014
In Proceedings
420
By Mitchell McLaren, Nicolas Scheffer, Luciana Ferrer, Yun Lei

This article proposes a new approach for contextualizing features for speaker recognition through the discrete cosine transform (DCT).

May, 2014
In Proceedings
420

Though sparse features have produced significant gains over traditional dense features in statistical machine translation, careful feature selection and feature engineering are necessary to avoid overfitting in optimizations.

May, 2014
In Proceedings
420
By Wen Wang, Vikramjit Mitra

Accurate phone-level segmentation of speech remains an important task for many subfields of speech research.

May, 2014
In Proceedings
420

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