Hearing aids (HAs) often feature different signal processing algorithms to optimize speech recognition (SR) in a given acoustic environment. In this paper, we explore if models that predict SR performance of hearing-impaired (HI), aided users are applicable to automatically select the best algorithm. To this end, SR experiments are conducted with 19 HI subjects who are aided with an open-source HA. Listeners’ SR is measured in virtual, complex acoustic scenes with two distinct noise conditions using the different speech enhancement strategies implemented in this HA. For model-based selection, we apply a PHOneme-based Binaural Intelligibility model (PHOBI) based on our previous work and extended with a component for simulating hearing loss. The non-intrusive model utilizes a deep neural network to predict phone probabilities; the deterioration of these phone representations in the presence of noise or generally signal degradation is quantified and used as model output. PHOBI model is trained with 960 h of English speech signals, a broad range of noise signals and room impulse responses. The performance of model-based algorithm selection is measured with two metrics: (i) Its ability to rank the HA algorithms in the order of subjective SR results and (ii) the SR difference between the measured best algorithm and the model-based selection ( Δ SR). Results are compared to selections obtained with one non-intrusive and two intrusive models. PHOBI outperforms the non-intrusive and one of the intrusive models in both noise conditions, achieving significantly higher correlations ( r = 0 . 63 and 0.80). Δ SR scores are significantly lower (better) compared to the non-intrusive baseline (3.5% and 4.6% against 8.6% and 9.8%, respectively). The results in terms of Δ SR between PHOBI and the intrusive models are statistically not different, although PHOBI operates on the observed signal alone and does not require a clean reference signal. • A DNN-based model accurately predicts the hearing aid algorithm that optimizes speech recognition for its user. • Individual predictions are made for 19 hearing-impaired, aided users in complex acoustic scenes. • The DNN-based approach is non-intrusive and performs equally well as established, intrusive models for speech recognition prediction.

Deep learning has the potential to enhance speech signals and increase their intelligibility for users of hearing aids. Deep models suited for real-world application should feature a low computational complexity and low processing delay of only a few milliseconds. In this paper, we explore deep speech enhancement that matches these requirements and contrast monaural and binaural processing algorithms in two complex acoustic scenes. Both algorithms are evaluated with objective metrics and in experiments with hearing-impaired listeners performing a speech-in-noise test. Results are compared to two traditional enhancement strategies, i.e., adaptive differential microphone processing and binaural beamforming. While in diffuse noise, all algorithms perform similarly, the binaural deep learning approach performs best in the presence of spatial interferers. Through a post-analysis, this can be attributed to improvements at low SNRs and to precise spatial filtering.

This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC). The DTLN combines a short-time Fourier transform and a learned feature representation in a stacked network approach, which enables robust information processing in the time-frequency and in the time domain, which also includes phase information. The model is only trained on 60 h of real and synthetic echo scenarios. The training setup includes multi-lingual speech, data augmentation, additional noise and reverberation to create a model that should generalize well to a large variety of real-world conditions. The DTLN approach produces state-of-the-art performance on clean and noisy echo conditions reducing acoustic echo and additional noise robustly. The method outperforms the AEC-Challenge baseline by 0.30 in terms of Mean Opinion Score (MOS).

This paper introduces a dual-signal transformation LSTM network (DTLN) for real-time speech enhancement as part of the Deep Noise Suppression Challenge (DNS-Challenge). This approach combines a short-time Fourier transform (STFT) and a learned analysis and synthesis basis in a stacked-network approach with less than one million parameters. The model was trained on 500 h of noisy speech provided by the challenge organizers. The network is capable of real-time processing (one frame in, one frame out) and reaches competitive results. Combining these two types of signal transformations enables the DTLN to robustly extract information from magnitude spectra and incorporate phase information from the learned feature basis. The method shows state-of-the-art performance and outperforms the DNS-Challenge baseline by 0.24 points absolute in terms of the mean opinion score (MOS).