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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
413

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

May, 2014
In Proceedings
413

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
413
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
413

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
413

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
413

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
413

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
413

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

May, 2014
In Proceedings
413

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

May, 2014
In Proceedings
413

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