Robust Speech Enhancement Using Statistical Signal Processing and Machine-Learning (Robuste Sprachverbesserung mittels statistischer Signalverarbeitung und maschinellem Lernen)

With the availability of powerful mobile electronic devices, speech communication plays an important role in many applications such as telecommunications, hearing aids and voice-controlled devices. Due to their mobility, such devices are often used in noisy acoustic environments. In such situations, the microphones do not only capture the desired speech signal but also undesired background noises. This degrades the perceived quality and the intelligibility of the speech signal. Further, the performance of subsequent speech processing algorithms may be impaired by background noises. To restore the quality and possibly also the intelligibility of noise corrupted speech, speech enhancement algorithms are employed. In this thesis, single-channel speech enhancement algorithms that either process the signal captured by a single microphone or the output of a spatial filtering algorithm are considered. The aim of this thesis is to increase the robustness of machine-learning (ML)-based and non-ML-based single-channel speech enhancement algorithms by exploiting synergies between both approaches. In conventional non-ML-based speech enhancement such as Wiener filtering based approaches, spectral gain functions are applied to the complex coefficients of the short-time Fourier spectra to enhance the noisy input signal. These gain functions are derived in a statistical framework where the clean speech and the noise Fourier coefficients are modeled using parametric probability density functions (PDFs). The parameters of the PDFs are estimated blindly from the noisy observation. Contrarily, ML-based algorithms use representative examples to learn the statistics of speech and noise which are then used for the enhancement. Often, ML-based approaches are motivated by the fact that conventional approaches are unable to follow highly non-stationary background noise types. However, it is still unclear how well ML-based approaches generalize unseen acoustic conditions. The first part of this thesis deals with non-ML-based noise power spectral density (PSD) estimators that rely on first-order recursive smoothing filter structures. In contrast to usual linear smoothing filters, the considered noise PSD estimators adaptively change the smoothing factor based on the previously estimated noise PSD and the noisy input. We show that such noise PSD estimators are generally biased and present approaches to analytically quantify and compensate for the bias. Second, we address a specific group of speech enhancement approaches where the speech PSD estimates are obtained using ML techniques. As the considered techniques only represent coarse spectral envelopes of speech, we refer to them as machine-learning spectral envelope (MLSE)-based approaches. The coarse speech PSD estimates of an MLSE approach result in an overestimation of the speech PSD between speech spectral harmonics. As a consequence, noise between these harmonics is not suppressed, if Gaussian speech enhancement filters, e.g., the Wiener filter, are employed. As a result, the enhanced signal exhibits noise bursts in speech active segments which reduce the perceived quality. Our analysis shows that super-Gaussian estimators are able to suppress the background noise even if the speech PSD is overestimated. Correspondingly, we propose to use these estimators to improve the quality of MLSE speech enhancement approaches. Further, an alternative approach to suppress the noise between speech spectral harmonics is proposed. Instead of using super-Gaussian models, an ML and a non-ML-based approach are combined. In the last part of the thesis, the generalization of unseen noise conditions of deep neural network (DNN)-based enhancement schemes is considered. To make the ML approach more robust to unseen noise conditions, it is proposed to use normalized features based on speech and noise PSD estimates obtained from conventional non-ML-based enhancement algorithms. More specifically, we propose to use the a priori signal-to-noise ratio (SNR), i.e., the ratio between the speech PSD and the noise PSD, and the a posteriori SNR, i.e., the ratio between the noisy periodogram and the noise PSD, as input features. In comparison to the already existing noise aware training approaches, where an estimate of the noise PSD is appended to the features extracted from the noisy observation, the proposed approach has two major advantages: First, the proposed features are scaleinvariant, i.e., their value is not influenced by the overall level of the input signal. As a result, also the performance of the DNN-based speech enhancement scheme becomes independent of the overall signal level. Second, the results show that the proposed features generally outperform noise aware training features in terms of enhancement quality in unseen noise conditions.