A previous signal processing algorithm that aimed to enhance spectral changes (SCE) over time showed benefit for hearing-impaired (HI) listeners to recognize speech in background noise. In this work, the previous SCE was manipulated to perform on target-dominant segments, rather than treating all frames equally. Instantaneous signal-to-noise ratios (SNRs) were calculated to determine whether the segments should be processed. Initially, the ideal SNR calculated by the knowledge of premixed signals was introduced to the previous SCE algorithm (SCE-iSNR). Speech intelligibility (SI) and clarity preference were measured for 12 HI listeners in steady speech-spectrum noise (SSN) and six-talk speech (STS) maskers, respectively. The results showed the SCE-iSNR algorithm improved SI significantly for both maskers at high signal-to-masker ratios (SMRs) and for STS masker at low SMRs, while processing effect on speech quality was small. Secondly, the estimated SNR obtained from real mixtures was used, resulting in another SCE-eSNR. SI and subjective rating on naturalness and speech quality were tested for 7 HI subjects. The SCE-eSNR algorithm showed improved SI for SSN masker at high SMRs and for STS masker at low SMRs, as well as better naturalness and speech quality for STS masker. The limitations of applying the algorithms are discussed.
An accurate objective speech intelligibility prediction algorithms is of great interest for many applications such as speech enhancement for hearing aids. Most algorithms measures the signal-to-noise ratios or correlations between the acoustic features of clean reference signals and degraded signals. However, these hand-picked acoustic features are usually not explicitly correlated with recognition. Meanwhile, deep neural network (DNN) based automatic speech recogniser (ASR) is approaching human performance in some speech recognition tasks. This work leverages the hidden representations from D…
Phase serves as a critical component of speech that influences the quality and intelligibility. Current speech enhancement algorithms are beginning to address phase distortions, but the algorithms focus on normal-hearing (NH) listeners. It is not clear whether phase enhancement is beneficial for hearing-impaired (HI) listeners. We investigated the influence of phase distortion on speech quality through a listening study, in which NH and HI listeners provided speech-quality ratings using the MUSHRA procedure. In one set of conditions, the speech was mixed with babble noise at 4 different signal…
We describe Parrotron, an end-to-end-trained speech-to-speech conversion model that maps an input spectrogram directly to another spectrogram, without utilizing any intermediate discrete representation. The network is composed of an encoder, spectrogram and phoneme decoders, followed by a vocoder to synthesize a time-domain waveform. We demonstrate that this model can be trained to normalize speech from any speaker regardless of accent, prosody, and background noise, into the voice of a single canonical target speaker with a fixed accent and consistent articulation and prosody. We further show…
University of Manchester（曼彻斯特大学）；University of Cambridge（剑桥大学）
Text-to-speech (TTS) systems offer the opportunity to compensate for a hearing loss at the source rather than correcting for it at the receiving end. This removes limitations such as time constraints for algorithms that amplify a sound in a hearing aid and can lead to higher speech quality. We propose an algorithm that restores loudness to normal perception at a high resolution in time, frequency and level, and embed it in a TTS system that uses Tacotron2 and WaveGlow to produce individually amplified speech. Subjective evaluations of speech quality showed that the proposed algorithm led to hi…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
A speech-based hearing test is designed to identify the susceptible error-prone phones for individual hearing impaired (HI) ear. Only robust tokens in the experiment noise levels had been chosen for the test. The noise-robustness of tokens is measured as SNR90 of the token, which is the signal to the speech-weighted noise ratio where a normal hearing (NH) listener would recognize the token with an accuracy of 90% on average. Two sets of tokens T1 and T2 having the same consonant-vowels but different talkers with distinct SNR90 had been presented with flat gain at listeners' most comfortable le…
University of Crete（克里特大学）；Chania General Hospital（干尼亚综合医院）
In this work we evaluate a neural based speech intelligibility booster based on spectral shaping and dynamic range compression (SSDRC), referred to as WaveNet-based SSDRC (wSSDRC), using a recently designed Greek Harvard-style corpus. The corpus has been developed according to the format of the Harvard/IEEE sentences and offers the opportunity to apply neural speech enhancement models and examine their performance gain for Greek listeners. wSSDRC has been successfully tested for English material and speakers in the past. In this paper we revisit wSSDRC to perform a full scale evaluation of the…
Federal University of Technology – Paraná（巴拉那联邦技术大学）；Federal University of Santa Catarina（圣卡塔琳娜联邦大学）
The multichannel Wiener filter (MWF) and its variations have been extensively applied to binaural hearing aids. However, its major drawback is the distortion of the binaural cues of the residual noise, changing the original acoustic scenario, which is of paramount importance for hearing impaired people. The MWF-IC method was previously proposed for joint speech dereverberation and noise reduction, preserving the interaural coherence (IC) of diffuse noise fields. In this work, we propose a new variation of the MWF-IC for both speech dereverberation and noise reduction, which preserves the origi…
In the Clarity project, we will run a series of machine learning challenges to revolutionise speech processing for hearing devices. Over five years, there will be three paired challenges. Each pair will consist of a competition focussed on hearing-device processing ("enhancement") and another focussed on speech perception modelling ("prediction"). The enhancement challenges will deliver new and improved approaches for hearing device signal processing for speech. The parallel prediction challenges will develop and improve methods for predicting speech intelligibility and quality for hearing imp…
Audatic（Audatic公司）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Technische Universität Berlin（柏林工业大学）；Bernstein Center for Computational Neuroscience Berlin（柏林伯恩斯坦计算神经科学中心）
Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep learning-based algorithm that selectively suppresses noise while maintaining speech signals. The algorithm restores speech intelligibility for hearing aid users to the level of control subjects with normal hearing. It consists of a deep network that is trained on a large custom database of noisy speech signals and is further optimized by a neural ar…
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 sp…
Many hearing-impaired listeners struggle to localize sounds due to poor availability of binaural cues. Listeners with a cochlear implant and a contralateral hearing aid -- so-called bimodal listeners -- are amongst the worst performers, as both interaural time and level differences are poorly transmitted. We present a new method to enhance head shadow in the low frequencies. Head shadow enhancement is achieved with a fixed beamformer with contralateral attenuation in each ear. The method results in interaural level differences which vary monotonically with angle. It also improves low-frequency…
In their everyday life, the speech recognition performance of human listeners is influenced by diverse factors, such as the acoustic environment, the talker and listener positions, possibly impaired hearing, and optional hearing devices. Prediction models come closer to considering all required factors simultaneously to predict the individual speech recognition performance in complex acoustic environments. While such predictions may still not be sufficiently accurate for serious applications, they can already be performed and demand an accessible representation. In this contribution, an intera…
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Southern University of Science and Technology（南方科技大学）
Electrical hearing by cochlear implants (CIs) may be fundamentally different from acoustic hearing by normal-hearing (NH) listeners, presumably showing unequal speech quality perception in various noise environments. Noise reduction (NR) algorithms used in CI reduce the noise in favor of signal-to-noise ratio (SNR), regardless of plausible accompanying distortions that may degrade the speech quality perception. To gain better understanding of CI speech quality perception, the present work aimed investigating speech quality perception in a diverse noise conditions, including factors of speech/n…
Carl von Ossietzky University Oldenburg（奥尔登堡大学）；Fraunhofer IDMT（弗劳恩霍夫数字媒体技术研究所）
Listening to the audio of TV broadcast signals can be challenging for hearing-impaired as well as normal-hearing listeners, especially when background sounds are prominent or too loud compared to the speech signal. This can result in a reduced satisfaction and increased listening effort of the listeners. Since the broadcast sound is usually premixed, we perform a subjective evaluation for quantifying the potential of speech enhancement systems based on audio source separation and recurrent neural networks (RNN). Recently, RNNs have shown promising results in the context of sound source separat…
Listening in noisy environments can be difficult even for individuals with a normal hearing thresholds. The speech signal can be masked by noise, which may lead to word misperceptions on the side of the listener, and overall difficulty to understand the message. To mitigate hearing difficulties on listeners, a co-operative speaker utilizes voice modulation strategies like Lombard speech to generate noise-robust utterances, and similar solutions have been developed for speech synthesis systems. In this work, we propose an alternate solution of choosing noise-robust lexical paraphrases to repres…
dCoNNear: An Artifact-Free Neural Network Architecture for Closed-loop Audio Signal Processing
Hearing Technology Lab, Department of Information Technology, Ghent University（信息科技系听力技术实验室，根特大学）；IDLAB, Department of Information Technology, Ghent University（信息科技系IDLAB，根特大学）
The estimation of speech intelligibility is still far from being a solved problem. Especially one aspect is problematic: most of the standard models require a clean reference signal in order to estimate intelligibility. This is an issue of some significance, as a reference signal is often unavailable in practice. In this work, therefore a non-intrusive speech intelligibility estimation framework is presented. In it, human listeners' performance in keyword recognition tasks is predicted using intelligibility measures that are derived from models trained for automatic speech recognition (ASR). O…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
In this article, we provide a model to estimate a real-valued measure of the intelligibility of individual speech segments. We trained regression models based on Convolutional Neural Networks (CNN) for stop consonants \textipa{/p,t,k,b,d,g/} associated with vowel \textipa{/A/}, to estimate the corresponding Signal to Noise Ratio (SNR) at which the Consonant-Vowel (CV) sound becomes intelligible for Normal Hearing (NH) ears. The intelligibility measure for each sound is called SNR$_{90}$, and is defined to be the SNR level at which human participants are able to recognize the consonant at least…
University of Engineering and Technology Peshawar（白沙瓦工程技术大学）；Qatar University（卡塔尔大学）
Reverberations are unavoidable in enclosures, resulting in reduced intelligibility for hearing impaired and non native listeners and even for the normal hearing listeners in noisy circumstances. It also degrades the performance of machine listening applications. In this paper, we propose a novel approach of binaural dereverberation of a single speech source, using the differences in the interaural cues of the direct path signal and the reverberations. Two beamformers, spaced at an interaural distance, are used to extract the reverberations from the reverberant speech. The interaural cues gener…
University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；Qatar University（卡塔尔大学）；MCS, NUST（巴基斯坦国立科技大学军事学院）
Reverberation results in reduced intelligibility for both normal and hearing-impaired listeners. This paper presents a novel psychoacoustic approach of dereverberation of a single speech source by recycling a pre-trained binaural anechoic speech separation neural network. As training the deep neural network (DNN) is a lengthy and computationally expensive process, the advantage of using a pre-trained separation network for dereverberation is that the network does not need to be retrained, saving both time and computational resources. The interaural cues of a reverberant source are given to thi…
In noisy environments, speech can be hard to understand for humans. Spoken dialog systems can help to enhance the intelligibility of their output, either by modifying the speech synthesis (e.g., imitate Lombard speech) or by optimizing the language generation. We here focus on the second type of approach, by which an intended message is realized with words that are more intelligible in a specific noisy environment. By conducting a speech perception experiment, we created a dataset of 900 paraphrases in babble noise, perceived by native English speakers with normal hearing. We find that careful…
University of Sherbrooke（舍布鲁克大学）；Graz University of Technology（格拉茨工业大学）
In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech enhancement algorithm that combines the non-negative matrix factorization (NMF) dictionary learning algorithm with the generalized cross-correlation (GCC) spatial localization method. Using a pre-learned universal NMF dictionary, RT-GCC-NMF operates in a frame-by-frame fashion by associating individual dictionary atoms to target speech or background interference based on their estimated time-delay of arrivals (TDOA). We evaluate RT-GCC-NMF on two-channel mixtures of speech and real-world noise from the Signal Sepa…
Yuan Ze University（元智大学）；National Chi Nan University（国立暨南国际大学）
Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE) systems based on deep neural networks (DNNs) applied to a distributed microphone architecture, and then investigates the effectiveness of three different DNN-model structures. The first system constructs a DNN model for each microphone to enhance the recorded noisy speech signal, and the second system combines all the noisy recordings into a large feature structure that is then enhanced through a DNN model. As for …
The human brain contextually exploits heterogeneous sensory information to efficiently perform cognitive tasks including vision and hearing. For example, during the cocktail party situation, the human auditory cortex contextually integrates audio-visual (AV) cues in order to better perceive speech. Recent studies have shown that AV speech enhancement (SE) models can significantly improve speech quality and intelligibility in very low signal to noise ratio (SNR) environments as compared to audio-only SE models. However, despite significant research in the area of AV SE, development of real-time…
