Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
EURASIP Journal on Applied Signal Processing
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Transforming Binary Uncertainties for Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Blind Extraction of Dominant Target Sources Using ICA and Time-Frequency Masking
IEEE Transactions on Audio, Speech, and Language Processing
International Journal of Speech Technology
Computer Speech and Language
Uncertainty-based learning of acoustic models from noisy data
Computer Speech and Language
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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When a number of speakers are simultaneously active, for example in meetings or noisy public places, the sources of interest need to be separated from interfering speakers and from each other in order to be robustly recognized. Independent component analysis (ICA) has proven a valuable tool for this purpose. However, ICA outputs can still contain strong residual components of the interfering speakers whenever noise or reverberation is high. In such cases, nonlinear postprocessing can be applied to the ICA outputs, for the purpose of reducing remaining interferences. In order to improve robustness to the artefacts and loss of information caused by this process, recognition can be greatly enhanced by considering the processed speech feature vector as a random variable with time-varying uncertainty, rather than as deterministic. The aim of this paper is to show the potential to improve recognition of multiple overlapping speech signals through nonlinear postprocessing together with uncertainty-based decoding techniques.