The objective of this paper is threefold: (1) to provide an extensive review of signal subspace speech enhancement, (2) to derive an upper bound for the performance of these techniques, and (3) to present a comprehensive study of the potential of subspace filtering to increase the robustness of automatic speech recognisers against stationary additive noise distortions. Subspace filtering methods are based on the orthogonal decomposition of the noisy speech observation space into a signal subspace and a noise subspace. This decomposition is possible under the assumption of a low-rank model for speech, and on the availability of an estimate of the noise correlation matrix. We present an extensive overview of the available estimators, and derive a theoretical estimator to experimentally assess an upper bound to the performance that can be achieved by any subspace-based method. Automatic speech recognition (ASR) experiments with noisy data demonstrate that subspace-based speech enhancement can significantly increase the robustness of these systems in additive coloured noise environments. Optimal performance is obtained only if no explicit rank reduction of the noisy Hankel matrix is performed. Although this strategy might increase the level of the residual noise, it reduces the risk of removing essential signal information for the recogniser's back end. Finally, it is also shown that subspace filtering compares favourably to the well-known spectral subtraction technique.

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Subspace filtering is an extensively studied technique that has been proven very effective in the area of speech enhancement to improve the speech intelligibility. In this paper, we review different subspace estimation techniques (Minimum Variance, Least Squares, Singular Value Adaptation, Time Domain Constrained and Spectral Domain Constrained) in a modified singular value decomposition (SVD) framework, and investigate their capability to improve the noise robustness of speech recognisers. An extensive set of recognition experiments with the Resource Management (RM) database showed that significant reductions in WER can be obtained, both for the white noise and the coloured noise case. Unlike for speech enhancement approaches, we found that no truncation of the noisy signal subspace should be done to optimise the recognition accuracy.

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We present a new algorithm for the automatic estimation of the voicing cut-off frequency (VCO), i.e., the frequency that separates the periodic low-frequency part from the aperiodic high-frequency part in voiced segments of natural speech. Starting from the power spectrum of a two pitch period speech frame, we define the VCO to be located at the frequency for which the sum of the periodic and aperiodic energy in the spectral band below and above that frequency respectively, is maximised. By formulating the problem in terms of a score function we are able to apply a dynamic programming based smoothing technique. Remarkably smooth and accurate VCO contours were obtained, despite the simplicity of the proposed algorithm. In a formal evaluation the algorithm compares favourably to two existing VCO estimation techniques.

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