A robust algorithm for detecting speech segments using an entropic contrast
Khurram Waheed,Kimberly A. Weaver,Fathi M. A. Salam +2 more
- Vol. 3
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TLDR
An entropy based contrast function between the speech segments and the background noise is proposed, which exhibits better-behaved characteristics as compared to the energy-based methods.Abstract:
This paper addresses the issue of automatic word/sentence boundary detection in both quiet and noisy environments. We propose to use an entropy based contrast function between the speech segments and the background noise. A simplified data based scheme of computing the entropy of the speech data is presented. The entropy-based contrast exhibits better-behaved characteristics as compared to the energy-based methods. An adaptive threshold is used to determine the candidate speech segments, which are subjected to word/sentence constraints. Experimental. results show that this algorithm outperforms energy-based algorithms. The improved detection accuracy of speech segments results in at least 25% improvement of recognition performance for isolated speech and more than 16% for connected speech. For continuous speech, a preprocessing stage comprising of the proposed speech segment detection makes the overall HMM based scheme more computationally efficient by rejection of silence periods.read more
Citations
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References
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Book
Fundamentals of speech recognition
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Book
Discrete-Time Processing of Speech Signals
TL;DR: The preface to the IEEE Edition explains the background to speech production, coding, and quality assessment and introduces the Hidden Markov Model, the Artificial Neural Network, and Speech Enhancement.
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An improved endpoint detector for isolated word recognition
TL;DR: A hybrid end-point detector is proposed which gives a rejection rate of less than 0.5 percent, while providing recognition accuracy close to that obtained from hand-edited endpoints.
Proceedings Article
Robust entropy-based endpoint detection for speech recognition in noisy environments.
TL;DR: This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments that uses the spectral entropy to identify the speech segments accurately.
Journal ArticleDOI
A robust algorithm for word boundary detection in the presence of noise
J.-C. Junqua,Brian Mak,B. Reaves +2 more
TL;DR: This new algorithm identifies islands of reliability (essentially the portion of speech contained between the first and the last vowel) using time and frequency-based features and then applies a noise adaptive procedure to refine the boundaries.
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