Instance-Based Learning Algorithms
Machine Learning
Speech Communication - Eurospeech '91
Machine Learning
Using Model Trees for Classification
Machine Learning
Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Subjective comparison and evaluation of speech enhancement algorithms
Speech Communication
Objective comparison of speech enhancement algorithms under real world conditions
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Adaptive β-order generalized spectral subtraction for speech enhancement
Signal Processing
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Robust speech interaction in motorcycle environment
Expert Systems with Applications: An International Journal
Constrained iterative speech enhancement with application to speechrecognition
IEEE Transactions on Signal Processing
Affective speech interface in serious games for supporting therapy of mental disorders
Expert Systems with Applications: An International Journal
Modelling and Simulation in Engineering
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Based on the observation that dissimilar speech enhancement algorithms perform differently for different types of interference and noise conditions, we propose a context-adaptive speech pre-processing scheme, which performs adaptive selection of the most advantageous speech enhancement algorithm for each condition. The selection process is based on an unsupervised clustering of the acoustic feature space and a subsequent mapping function that identifies the most appropriate speech enhancement channel for each audio input, corresponding to unknown environmental conditions. Experiments performed on the MoveOn motorcycle speech and noise database validate the practical value of the proposed scheme for speech enhancement and demonstrate a significant improvement in terms of speech recognition accuracy, when compared to the one of the best performing individual speech enhancement algorithm. This is expressed as accuracy gain of 3.3% in terms of word recognition rate. The advance offered in the present work reaches beyond the specifics of the present application, and can be beneficial to spoken interfaces operating in fast-varying noise environments.