Evaluating Near End Listening Enhancement Algorithms in Realistic Environments

Speech playback (e.g., TV, radio, public address) becomes harder to understand in the presence of noise and reverberation. NELE (Near End Listening Enhancement) algorithms can improve intelligibility by modifying the signal before it is played back. Substantial intelligibility improvements have been achieved in the lab for both natural and synthetic speech. However, evidence is still scarce on how these algorithms work under conditions of realistic noise and reverberation. We present a realistic test platform, featuring two representative everyday scenarios in which speech playback may occur (in the presence of both noise and reverberation): a domestic space (living room) and a public space (cafeteria). The generated stimuli are evaluated by measuring keyword accuracy rates in a listening test with normal hearing subjects. We use the new platform to compare three state-of-theart NELE algorithms, employing either noise-adaptive or nonadaptive strategies, and with or without compensation for reverberation

Near end listening enhancement in realistic environments

Speech playback is harder to understand in noise. Near End Listening Enhancement algorithms try to overcomethe problem by enhancing the speech signal before it is played by a device. Different strategies have beentried, achieving variable degrees of success in specific noise conditions. Such technologies, however, are oftentested in artificial settings - with controlled noise sources and no reverberation. The purpose of this study isto compare a set of state-of-the-art algorithms based on different approaches (adaptive vs non-adaptive, withor without a compensation for reverberation) in simulated real- life scenarios. Binaural noise recordings andimpulse responses of real environments have been used to create two representative scenarios in which speechplayback may occur, namely a domestic and a public space. A preliminary study with N=24 subjects revealedthe need for higher SNRs in the realistic settings (in comparison to controlled noise conditions) in order to obtainthe same levels of intelligibility for plain speech. The goal of the main study is to assess the impact of noiseadaptivity and reverberation awareness in realistic scenarios, in order to better understand how to make speechplayback more robust to noise in real-life situations

An evaluation of intrusive instrumental intelligibility metrics

Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and $\text{sEPSM}^\text{corr}$. In addition, this paper investigates the ability of intelligibility metrics to generalize to new types of distortions and analyzes why the top performing metrics have high performance. The intelligibility data were obtained from 11 listening tests described in the literature. The stimuli included Dutch, Danish, and English speech that was distorted by additive noise, reverberation, competing talkers, pre-processing enhancement, and post-processing enhancement. SIIB and HASPI had the highest performance achieving a correlation with listening test scores on average of $\rho=0.92$ and $\rho=0.89$, respectively. The high performance of SIIB may, in part, be the result of SIIBs developers having access to all the intelligibility data considered in the evaluation. The results show that intelligibility metrics tend to perform poorly on data sets that were not used during their development. By modifying the original implementations of SIIB and STOI, the advantage of reducing statistical dependencies between input features is demonstrated. Additionally, the paper presents a new version of SIIB called $\text{SIIB}^\text{Gauss}$, which has similar performance to SIIB and HASPI, but takes less time to compute by two orders of magnitude.Comment: Published in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 201

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

Evaluation of the sparse coding shrinkage noise reduction algorithm for the hearing impaired

Although there are numerous single-channel noise reduction strategies to improve speech perception in a noisy environment, most of them can only improve speech quality but not improve speech intelligibility for normal hearing (NH) or hearing impaired (HI) listeners. Exceptions that can improve speech intelligibility currently are only those that require a priori statistics of speech or noise. Most of the noise reduction algorithms in hearing aids are adopted directly from the algorithms for NH listeners without taking into account of the hearing loss factors within HI listeners. HI listeners suffer more in speech intelligibility than NH listeners in the same noisy environment. Further study of monaural noise reduction algorithms for HI listeners is required.The motivation is to adapt a model-based approach in contrast to the conventional Wiener filtering approach. The model-based algorithm called sparse coding shrinkage (SCS) was proposed to extract key speech information from noisy speech. The SCS algorithm was evaluated by comparison with another state-of-the-art Wiener filtering approach through speech intelligibility and quality tests using 9 NH and 9 HI listeners. The SCS algorithm matched the performance of the Wiener filtering algorithm in speech intelligibility and speech quality. Both algorithms showed some intelligibility improvements for HI listeners but not at all for NH listeners. The algorithms improved speech quality for both HI and NH listeners.Additionally, a physiologically-inspired hearing loss simulation (HLS) model was developed to characterize hearing loss factors and simulate hearing loss consequences. A methodology was proposed to evaluate signal processing strategies for HI listeners with the proposed HLS model and NH subjects. The corresponding experiment was performed by asking NH subjects to listen to unprocessed/enhanced speech with the HLS model. Some of the effects of the algorithms seen in HI listeners are reproduced, at least qualitatively, by using the HLS model with NH listeners.Conclusions: The model-based algorithm SCS is promising for improving performance in stationary noise although no clear difference was seen in the performance of SCS and a competitive Wiener filtering algorithm. Fluctuating noise is more difficult to reduce compared to stationary noise. Noise reduction algorithms may perform better at higher input signal-to-noise ratios (SNRs) where HI listeners can get benefit but where NH listeners already reach ceiling performance. The proposed HLS model can save time and cost when evaluating noise reduction algorithms for HI listeners

Speech Enhancement for Automatic Analysis of Child-Centered Audio Recordings

Analysis of child-centred daylong naturalist audio recordings has become a de-facto research protocol in the scientific study of child language development. The researchers are increasingly using these recordings to understand linguistic environment a child encounters in her routine interactions with the world. These audio recordings are captured by a microphone that a child wears throughout a day. The audio recordings, being naturalistic, contain a lot of unwanted sounds from everyday life which degrades the performance of speech analysis tasks. The purpose of this thesis is to investigate the utility of speech enhancement (SE) algorithms in the automatic analysis of such recordings. To this effect, several classical signal processing and modern machine learning-based SE methods were employed 1) as a denoiser for speech corrupted with additive noise sampled from real-life child-centred daylong recordings and 2) as front-end for downstream speech processing tasks of addressee classification (infant vs. adult-directed speech) and automatic syllable count estimation from the speech. The downstream tasks were conducted on data derived from a set of geographically, culturally, and linguistically diverse child-centred daylong audio recordings. The performance of denoising was evaluated through objective quality metrics (spectral distortion and instrumental intelligibility) and through the downstream task performance. Finally, the objective evaluation results were compared with downstream task performance results to find whether objective metrics can be used as a reasonable proxy to select SE front-end for a downstream task. The results obtained show that a recently proposed Long Short-Term Memory (LSTM)-based progressive learning architecture provides maximum performance gains in the downstream tasks in comparison with the other SE methods and baseline results. Classical signal processing-based SE methods also lead to competitive performance. From the comparison of objective assessment and downstream task performance results, no predictive relationship between task-independent objective metrics and performance of downstream tasks was found

Learning static spectral weightings for speech intelligibility enhancement in noise

Near-end speech enhancement works by modifying speech prior to presentation in a noisy environment, typically operating under a constraint of limited or no increase in speech level. One issue is the extent to which near-end enhancement techniques require detailed estimates of the masking environment to function effectively. The current study investigated speech modification strategies based on reallocating energy statically across the spectrum using masker-specific spectral weightings. Weighting patterns were learned offline by maximising a glimpse-based objective intelligibility metric. Keyword scores in sentences in the presence of stationary and fluctuating maskers increased, in some cases by very substantial amounts, following the application of masker- and SNR-specific spectral weighting. A second experiment using generic masker-independent spectral weightings that boosted all frequencies above 1 kHz also led to significant gains in most conditions. These findings indicate that energy-neutral spectral weighting is a highly-effective near-end speech enhancement approach that places minimal demands on detailed masker estimation

ICASSP 2023 Acoustic Echo Cancellation Challenge

The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic + buffering latency to 20ms, as well as including a full-band version of AECMOS. We open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 10,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework and provide an objective metric for researchers to quickly test their results. The winners of this challenge were selected based on the average mean opinion score (MOS) achieved across all scenarios and the word accuracy (WAcc) rate.Comment: arXiv admin note: substantial text overlap with arXiv:2202.13290, arXiv:2009.0497