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Purpose: A common way of eliciting speech from individuals is by using passages of written language that are intended to be read aloud. Read passages afford the opportunity for increased control over the phonetic properties of elicited speech, of which phonetic balance is an often-noted example. No comprehensive analysis of the phonetic balance of read passages has been reported in the literature. The present article provides a quantitative comparison of the phonetic balance of widely used passages in English. Method: Assessment of phonetic balance is carried out by comparing the distribution of phonemes in several passages to distributions consistent with typical spoken English. Data regarding the distribution of phonemes in spoken American English are aggregated from the published literature and large speech corpora. Phoneme distributions are compared using Spearman rank order correlation coefficient to quantify similarities of phoneme counts in those sources. Results: Correlations between phoneme distributions in read passages and aggregated material representative of spoken American English ranged from 0.70 to 0.89. Correlations between phoneme counts from all passages, literature sources, and corpus sources ranged from 0.55 to 0.99. All correlations were statistically significant at the Bonferroni-adjusted level. Conclusions: Passages considered in the present work provide high, but not ideal, phonetic balance. Space exists for the creation of new passages that more closely match the phoneme distributions observed in spoken American English. "The Caterpillar" provided the best phonetic balance, but phoneme distributions in all considered materials were highly similar to each other.

Occupational and recreational acoustic noise exposure is known to cause permanent hearing damage and reduced quality of life, which indicates the importance of noise controls including hearing protection devices (HPDs) in situations where high noise levels exist. While HPDs can provide adequate protection for many noise exposures, it is often a challenge to properly train HPD users and maintain compliance with usage guidelines. HPD fit-testing systems are commercially available to ensure proper attenuation is achieved, but they often require specific facilities designed for hearing testing (e.g., a quiet room or an audiometric booth) or special equipment (e.g., modified HPDs designed specifically for fit testing). In this study, we explored using visual information from a photograph of an HPD inserted into the ear to estimate hearing protector attenuation. Our dataset consists of 960 unique photographs from four types of hearing protectors across 160 individuals. We achieved 73% classification accuracy in predicting if the fit was greater or less than the median measured attenuation (29 dB at 1 kHz) using a deep neural network. Ultimately, the fit-test technique developed in this research could be used for training as well as for automated compliance monitoring in noisy environments to prevent hearing loss. [ABSTRACT FROM AUTHOR]

Many individuals struggle to understand speech in listening scenarios that include reverberation and background noise. An individual's ability to understand speech arises from a combination of peripheral auditory function, central auditory function, and general cognitive abilities. The interaction of these factors complicates the prescription of treatment or therapy to improve hearing function. Damage to the auditory periphery can be studied in animals; however, this method alone is not enough to understand the impact of hearing loss on speech perception. Computational auditory models bridge the gap between animal studies and human speech perception. Perturbations to the modeled auditory systems can permit mechanism-based investigations into observed human behavior. In this study, we propose a computational model that accounts for the complex interactions between different hearing damage mechanisms and simulates human speech-in-noise perception. The model performs a digit classification task as a human would, with only acoustic sound pressure as input. Thus, we can use the model's performance as a proxy for human performance. This two-stage model consists of a biophysical cochlear-nerve spike generator followed by a deep neural network (DNN) classifier. We hypothesize that sudden damage to the periphery affects speech perception and that central nervous system adaptation over time may compensate for peripheral hearing damage. Our model achieved human-like performance across signal-to-noise ratios (SNRs) under normal-hearing (NH) cochlear settings, achieving 50% digit recognition accuracy at −20.7 dB SNR. Results were comparable to eight NH participants on the same task who achieved 50% behavioral performance at −22 dB SNR. We also simulated medial olivocochlear reflex (MOCR) and auditory nerve fiber (ANF) loss, which worsened digit-recognition accuracy at lower SNRs compared to higher SNRs. Our simulated performance following ANF loss is consistent with the hypothesis that cochlear synaptopathy impacts communication in background noise more so than in quiet. Following the insult of various cochlear degradations, we implemented extreme and conservative adaptation through the DNN. At the lowest SNRs (<0 dB), both adapted models were unable to fully recover NH performance, even with hundreds of thousands of training samples. This implies a limit on performance recovery following peripheral damage in our human-inspired DNN architecture. [ABSTRACT FROM AUTHOR]

This paper reviews the current state of several formal models of speech motor control, with particular focus on the low-level control of the speech articulators. Further development of speech motor control models may be aided by a comparison of model attributes. The review builds an understanding of existing models from first principles, before moving into a discussion of several models, showing how each is constructed out of the same basic domain-general ideas and components—e.g., generalized feedforward, feedback, and model predictive components. This approach allows for direct comparisons to be made in terms of where the models differ, and their points of agreement. Substantial differences among models can be observed in their use of feedforward control, process of estimating system state, and method of incorporating feedback signals into control. However, many commonalities exist among the models in terms of their reliance on higher-level motor planning, use of feedback signals, lack of time-variant adaptation, and focus on kinematic aspects of control and biomechanics. Ongoing research bridging hybrid feedforward/feedback pathways with forward dynamic control, as well as feedback/internal model-based state estimation, is discussed. [ABSTRACT FROM AUTHOR]

Here we report the methods and output of a workshop examining possible futures of speech and hearing science out to 2030. Using a design thinking approach, a range of human-centered problems in communication were identified that could provide the motivation for a wide range of research. Nine main research programs were distilled and are summarized: (a) measuring brain and other physiological parameters, (b) auditory and multimodal displays of information, (c) auditory scene analysis, (d) enabling and understanding shared auditory virtual spaces, (e) holistic approaches to health management and hearing impairment, (f) universal access to evolving and individualized technologies, (g) biological intervention for hearing dysfunction, (h) understanding the psychosocial interactions with technology and other humans as mediated by technology, and (i) the impact of changing models of security and privacy. The design thinking approach attempted to link the judged level of importance of different research areas to the ‘‘end in mind’’ through empathy for the real-life problems embodied in the personas created during the workshop. [ABSTRACT FROM AUTHOR]