Monaural intrusive speech intelligibility prediction (SIP) methods aim to predict the speech intelligibility (SI) of a single-microphone noisy and/or processed speech signal using the underlying clean speech signal. In the present work, we propose a neural network for monaural intrusive SIP. The proposed network is trained on data from multiple listening tests to predict SI. In the interest of using the available listening test data as efficiently as possible and to facilitate SI prediction of short duration speech signals, training is based on a local-time intelligibility curve derived from the listening test data. The trained neural network is evaluated, in terms of rank order correlation, against the classical monaural intrusive predictors STOI and ESTOI. The network is found to perform the best overall with a Kendall's tau of 0.825 measured over long duration, i.e. speech signals up to several minutes in duration. For short-term prediction using short speech signals of 1 - 10 seconds the network also shows better performance and smaller prediction variance.

Data-driven speech intelligibility prediction has been slow totake off. Datasets of measured speech intelligibility are scarce,and so current models are relatively small and rely on hand-picked features. Classical predictors based on psychoacousticmodels and heuristics are still the state-of-the-art. This workproposes a U-Net inspired fully convolutional neural networkarchitecture, NSIP, trained and tested on ten datasets to pre-dict intelligibility of time-domain speech. The architecture iscompared to a frequency domain data-driven predictor and tothe classical state-of-the-art predictors STOI, ESTOI, HASPIand SIIB. The performance of NSIP is found to be superior fordatasets seen in the training phase. On unseen datasets NSIPreaches performance comparable to classical predictors.

Speech Intelligibility Prediction (SIP) algorithms are becoming popular tools within the development and operation of speech processing devices and algorithms. However, many SIP algorithms require knowledge of the underlying clean speech; a signal that is often not available in real-world applications. This has led to increased interest in nonintrusive SIP algorithms, which do not require clean speech to make predictions. In this paper, we investigate the use of Convolutional Neural Networks (CNNs) for nonintrusive SIP. To do so, we utilize a CNN architecture that shows similarities to existing SIP algorithms, in terms of computational structure, and which allows for easy and meaningful visualization and interpretation of trained weights. We evaluate this architecture using a large dataset obtained by combining datasets from the literature. The proposed method shows high prediction performance when compared with four existing intrusive and nonintrusive SIP algorithms. This demonstrates the potential of deep learning for speech intelligibility prediction.

We propose a non-intrusive intelligibility measure for noisy and non-linearly processed speech, i.e. a measure which can predict intelligibility from a degraded speech signal without requiring a clean reference signal. The proposed measure is based on the Short-Time Objective Intelligibility (STOI) measure. In particular, the non-intrusive STOI measure estimates clean signal amplitude envelopes from the degraded signal. Subsequently, the STOI measure is evaluated by use of the envelopes of the degraded signal and the estimated clean envelopes. The performance of the proposed measure is evaluated on a dataset including speech in different noise types, processed with binary masks. The measure is shown to predict intelligibility well in all tested conditions, with the exception of those including a single competing speaker. While the measure does not perform as well as the original (intrusive) STOI measure, it is shown to outperform existing non-intrusive measures.

Speech intelligibility prediction methods are popular tools within the speech processing community for objective evaluation of speech intelligibility of e.g.enhanced speech.The Short-Time Objective Intelligibility (STOI) measure has become highly used due to its simplicity and high prediction accuracy.In this paper we investigate the use of Band Importance Functions (BIFs) in the STOI measure, i.e. of unequally weighting the contribution of speech information from each frequency band.We do so by fitting BIFs to several datasets of measured intelligibility, and cross evaluating the prediction performance.Our findings indicate that it is possible to improve prediction performance in specific situations.However, it has not been possible to find BIFs which systematically improve prediction performance beyond the data used for fitting.In other words, we find no evidence that the performance of the STOI measure can be improved considerably by extending it with a non-uniform BIF.

We propose and evaluate a binaural speech intelligibility measure. The measure is a binaural extension of the Short-Time Objective Intelligibility (STOI) measure and focuses on predicting the intelligibility of noisy speech which has been enhanced by a speech processing algorithm (e.g. in a hearing aid). We show that the measure can accurately predict 1) the Speech Reception Threshold (SRT) for a frontal speaker masked by a point noise source in the horizontal plane, 2) the improvement in SRT obtained by independently processing the left and right ear signals with Ideal Time Frequency Segregation (ITFS), and 3) the intelligibility of speech in the presence of multiple interferers as well as the effect of processing the noisy signals with 2-microphone MVDR beamforming as used in hearing aids. Finally, we show that the computational demands associated with the measure are favourable in comparison with those of a previously proposed measure with similar properties.

Objective speech intelligibility measures are gaining popularity in the development of speech enhancement algorithms and speech processing devices such as hearing aids. Such devices may process the input signals nonlinearly and modify the binaural cues presented to the user. We propose a method for predicting the intelligibility of noisy and nonlinearly processed binaural speech. This prediction is based on the noisy and processed signal as well as a clean speech reference signal. The method is obtained by extending a modified version of the short-time objective intelligibility (STOI) measure with a modified equalization-cancellation (EC) stage. We evaluate the performance of the method by comparing the predictions with measured intelligibility from four listening experiments. These comparisons indicate that the proposed measure can provide accurate predictions of (1) the intelligibility of diotic speech with an accuracy similar to that of the original STOI measure, (2) speech reception thresholds (SRTs) in conditions with a frontal target speaker and a single interferer in the horizontal plane, (3) SRTs in conditions with a frontal target and a single interferer when ideal time frequency segregation (ITFS) is applied to the left and right ears separately, and (4) the advantage of two-microphone beamforming as applied in state-of-the-art hearing aids. A MATLAB implementation of the proposed measure is available online1.

Objective intelligibility measures are increasingly being used to assess the performance of speech processing algorithms, e.g. for hearing aids. It has been shown that the short time objective intelligibility (STOI) measure yields good results in this respect. In this paper we propose a binaural extension of the STOI measure, which predicts binaural advantage using a modified equalization cancellation (EC) stage. The proposed method is evaluated for a range of acoustic conditions. Firstly, the method is able to predict the advantage of spatial separation between a speech target and a speech shaped noise (SSN) interferer. Secondly, the method yields results comparable to the monaural STOI measure when presented with noisy speech processed by ideal time-frequency segregation (ITFS). Finally, the method also performs well when presented with a selection of different acoustic conditions combined with beamforming as used in hearing aids.