A robust algorithm for accurate endpointing of speech signals
Speech Communication
Speech Communication - Special issue on speech processing in adverse conditions
A pitch determination and voiced/unvoiced decision algorithm for noisy speech
Speech Communication
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Speech detection in noisy environments by wavelet energy-based recurrent neural fuzzy network
Expert Systems with Applications: An International Journal
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
A recurrent neural fuzzy network for word boundary detection invariable noise-level environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Fuzzy Systems
Type-2 fuzzy hidden Markov models and their application to speech recognition
IEEE Transactions on Fuzzy Systems
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IEEE Journal on Selected Areas in Communications
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
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This paper proposes a new method to detect the boundary of speech in noisy environments. This detection method uses Haar wavelet energy and entropy (HWEE) as detection features. The Haar wavelet energy (HWE) is derived by using the robust band that shows the most significant difference between speech and nonspeech segments at different noise levels. Similarly, the wavelet energy entropy (WEE) is computed by selecting the two wavelet energy bands whose entropy shows the most significant speech/nonspeech difference. The HWEE features are fed as inputs to a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for classification. The RSEIT2FNN is used because it uses type-2 fuzzy sets, which are more robust to noise than type-1 fuzzy sets. The recurrent structure in the RSEIT2FNN helps to remember the context information of a test frame. The RSEIT2FNN outputs are compared with a parameter threshold to determine whether it is a speech or nonspeech period. The HWEE-based RSEIT2FNN detection was applied to speech detection in different noisy environments with different noise levels. Comparisons with different detection methods verified the advantage of the proposed method of using HWEE.