Towards a new speech event detection approach for landmark-based speech recognition

In this work, we present a new approach for the classification and detection of speech units for the use in landmark or event-based speech recognition systems. We use segmentation to model any time-variable speech unit by a fixed-dimensional observation vector, in order to train a committee of boost...

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Bibliographic Details
Published in:	2012 IEEE Spoken Language Technology Workshop (SLT) pp. 342 - 347
Main Authors:	Ziegler, S., Ludusan, B., Gravier, G.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-12-2012
Subjects:	Accuracy Boosting Hidden Markov models landmark-driven ASR Speech speech event detection Speech recognition Training Vectors
Online Access:	Get full text
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Summary:	In this work, we present a new approach for the classification and detection of speech units for the use in landmark or event-based speech recognition systems. We use segmentation to model any time-variable speech unit by a fixed-dimensional observation vector, in order to train a committee of boosted decision stumps on labeled training data. Given an unknown speech signal, the presence of a desired speech unit is estimated by searching for each time frame the corresponding segment, that provides the maximum classification score. This approach improves the accuracy of a phoneme classification task by 1.7%, compared to classification using HMMs. Applying this approach to the detection of broad phonetic landmarks inside a landmark-driven HMM-based speech recognizer significantly improves speech recognition.
ISBN:	9781467351256 1467351253
DOI:	10.1109/SLT.2012.6424247