Abstract
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.
Highlights
When speech recognition is to be used in arbitrary, noisy environments, interfering speech poses significant problems due to the ovelapping spectra and nonstationarity
In order to compensate for these losses and to obtain results exceeding those of independent component analysis (ICA) alone, we suggest the use of uncertainty-of-observation techniques for the subsequent speech recognition
This can be achieved by the so-called “uncertainty propagation,” which converts an uncertain description of speech from the spectrum domain, where ICA takes place, to the feature domain of speech recognition
Summary
When speech recognition is to be used in arbitrary, noisy environments, interfering speech poses significant problems due to the ovelapping spectra and nonstationarity. In order to compensate for these losses and to obtain results exceeding those of ICA alone, we suggest the use of uncertainty-of-observation techniques for the subsequent speech recognition From such an uncertain description of the speech signal in the spectrum domain, uncertainties need to be made available in the feature domain, in order to be used for recognition.
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