Many single- and multi-channel speech enhancement techniques, old and new, rely in one way or another on estimates of the noise power spectral density (PSD). For example, the classical Wiener filter requires that either the speech or noise PSD be estimated. Typically, the noise PSD is estimated, as it is often easier to model and estimate than the speech. As a result, much attention has been paid to this important problem over the past couple of decades, with important scientific milestones being the minimum statistics (MS), the minima controlled recursive averaging (IMCRA), and the minimum mean squared (MMSE) estimators. Despite leading to major progress, these estimators are rather ad hoc, making them difficult to tune and improve in a systematic manner. In this article, we analyse some of the common heuristics employed in such noise PSD estimators to put them on firmer mathematical ground. More specifically, we use the Gaussian stochastic volatility model and show that the MMSE noise PSD estimator can be interpreted as a special case thereof. Moreover, we analyze the related problem of speech presence probability (SPP) estimation and show that the SPP estimation performed in the MMSE noise PSD estimator can be interpreted as an SNR estimator in the context of the Gaussian stochastic volatility model.

The understanding of speech from a particular speaker in the presence of other interfering speakers can be severely degraded for a hearing impaired person. Beamforming techniques have been proven to be effective to improve the speech understanding in such scenarios. However, the number of microphones in a hearing aid (HA) is limited due to the space and power constraints present in the HA. In this paper, we propose to use an external device e.g., a microphone array, that can communicate with the HA to overcome this limitation. We propose a method to control this external device based on the look direction of the HA user. We show, by means of simulations, the robustness of the proposed method at very low SNRs in a reverberant scenario. Moreover, we have also conducted experiments that show the benefit of using this framework for binaural and monaural enhancement.

In recent years, research into objective speech intelligibil- ity measures has gained increased interest as a tool to opti- mize speech enhancement algorithms. While most intelligi- bility measures are intrusive, i.e., they require a clean refer- ence signal, this is rarely available in real-time applications. This paper proposes two non-intrusive intelligibility measures, which allow using the intrusive short-time objective intelligibil- ity (STOI) measure without requiring access to the clean signal. Instead, a reference signal is obtained from the degraded sig- nal using either a fixed or an adaptive harmonic spatial filter. This reference signal is then used as input to STOI. The exper- imental results show a high correlation between both proposed non-intrusive speech intelligibility measures and the original in- trusively computed STOI scores.

In recent years, objective measures of speech intelligibility have gained increasing interest. However, most speech intelligibil- ity metrics require a clean reference signal, which is often not available in real-life applications. In a recent publication, we proposed a method, the Non-Intrusive Codebook-based Short- Time Objective Intelligibility (NIC-STOI) metric, which allows using an intrusive method without requiring access to the clean signal. The statistics of the reference signal is estimated as a combination of predefined codebooks that best fit the degraded signal by modeling the speech and noisy spectra. In this pa- per, we perform additional validation of the NIC-STOI in more diverse noise condition as well as for speech processed non- linearly with binary masks, where it is shown to outperform existing non-intrusive metrics.

Speech intelligibility is often severely degraded among hearing impaired individuals in situations such as the cocktail party scenario. The performance of the current hearing aid technology has been observed to be limited in these scenarios. In this paper, we propose a binaural speech enhancement framework that takes into consideration the speech production model. The enhancement framework proposed here is based on the Kalman filter that allows us to take the speech production dynamics into account during the enhancement process. The usage of a Kalman filter requires the estimation of clean speech and noise short term predictor (STP) parameters, and the clean speech pitch parameters. In this work, a binaural codebook-based method is proposed for estimating the STP parameters, and a directional pitch estimator based on the harmonic model and maximum likelihood principle is used to estimate the pitch parameters. The proposed method for estimating the STP and pitch parameters jointly uses the information from left and right ears, leading to a more robust estimation of the filter parameters. Objective measures such as PESQ and STOI have been used to evaluate the enhancement framework in different acoustic scenarios representative of the cocktail party scenario. We have also conducted subjective listening tests on a set of nine normal hearing subjects, to evaluate the performance in terms of intelligibility and quality improvement. The listening tests show that the proposed algorithm, even with access to only a single channel noisy observation, significantly improves the overall speech quality, and the speech intelligibility by up to 15 %.

The estimation of the noise power spectral density (PSD) forms a critical component of several existing single channel speech enhancement systems. In this paper, we evaluate one new and some of the existing and commonly used noise PSD estimation algorithms in terms of the spectral estimation accuracy and the enhancement performance for different commonly encountered background noises, which are stationary and non-stationary in nature. The evaluated algorithms include the Minimum Statistics, MMSE, IMCRA methods and a new model-based method.

Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay to give good results in non- stationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estima- tors in terms of tracking speed and estimation accuracy.

This paper deals with the enhancement of speech in presence of non-stationary babble noise. A binaural speech enhancement framework is proposed which takes into account both the voiced and unvoiced speech production model. The usage of this model in enhancement requires the Short term predictor (STP) parameters and the pitch information to be estimated. This paper uses a codebook based approach for estimating the STP parameters and a parametric binaural method is proposed for estimating the pitch parameters. Improvements in objective score are shown when using the voiced-unvoiced speech model in comparison to the conventional unvoiced speech model.

Automatic adjustment of the hearing aid according to the intelligibility for the user in the environment could be beneficial. While most intelligibility metrics require a clean speech reference, i.e. intrusive methods, this is rarely available in real-life. This paper proposes a non-intrusive intelligibility metric in which a reconstruction of the clean speech is used in the established intrusive short-time objective intelligibility (STOI) metric. The reconstruction of the clean speech is based on pitch-features of the desired source using a spatio-temporal harmonic model. This model takes advantage of both the spatial and spectral separation of the desired source and interferers to reconstruct the clean signal. The simulations show a high correlation between the proposed pitch-based STOI (PB-STOI) and the original intrusive STOI and hence is promising for online processing of intelligibility.

This paper investigates the potential performance of generalized subspace filters for speech enhancement in cocktail party situations with very poor signal/noise ratio, e.g. down to -15 dB. Performance metrics output signal/noise ratio, signal/distortion ratio, speech quality rating and speech intelligibility rating are mapped as functions of two algorithm parameters, revealing clear trade-off options between noise, distortion and subjective performances and a recommended choice of trade-off. Given sufficiently good noise statistics, SNR improvements around 20 dB as well as PESQ quality and STOI intelligibility rating improvements exceeding 1.0 and 0.2 points respectively are found. This shows the potential of the method.

Enhancement of speech in non-stationary background noise is a challenging task, and conventional single channel speech enhancement algorithms have not been able to improve the speech intelligibility in such scenarios. The work proposed in this paper investigates a single channel Kalman filter based speech enhancement algorithm, whose parameters are estimated using a codebook based approach. The results indicate that the enhancement algorithm is able to improve the speech intelligibility and quality according to objective measures. Moreover, we investigate the effects of utilizing a speaker specific trained codebook over a generic speech codebook in relation to the performance of the speech enhancement system.

Recently, the ideal binary mask has been introduced in the modulation domain by extending the ideal channel selection method to modulation channel selection [1]. This new method shows substantial improvement in speech intelligibility but less than its predecessor despite the higher complexity. Here, we extend the previous finding from [1] and provide a more direct comparison of binary masking in the modulation domain with binary masking in the time-frequency domain. Subjective and objective evaluations are performed and provide additional insight into modulation domain processing.

Binary masking is a simple and efficient method for source separation, and a high increase in intelligibility can be obtained by applying the target binary mask to noisy speech. The target binary mask can only be calculated under ideal conditions and will contain errors when estimated in real-life applications. This paper proposes a method for correcting these errors. The error-correction is based on a hidden Markov model and uses the Viterbi algorithm to calculate the most probable error-free target binary mask from a target binary mask containing errors. The results demonstrate that it is possible to correct errors in the target binary mask and reduce the noise energy. However, speech energy is also reduced by the error-correction, but the impact on speech intelligibility and speech quality are not established or evaluated in the present study.