Objective To predict the impact of face personal protective equipment on verbal communication during the SARS-CoV-2 pandemic. Design We assessed the effect of common types and combinations of face personal protective equipment on speech intelligibility in quiet and in a simulated noisy environment. Results Wearing face personal protective equipment impairs transmission of middle-to-high voice frequencies and affects speech intelligibility. Surgical masks are responsible for up to 23.3% loss of speech intelligibility in noisy environments. The effects are larger in the condition of advanced face personal protective equipment, accounting for up to 69.0% reduction of speech intelligibility. Conclusion The use of face personal protective equipment causes significant verbal communication issues. Healthcare workers, school-aged children, and people affected by voice and hearing disorders may represent specific at-risk groups for impaired speech intelligibility.
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.
Speech enhancement is one of the biggest challenges in hearing prosthetics. In face-to-face communication devices have to estimate the signal of interest, but playback of speech signals from an electronic device opens up new opportunities. Audio signals can be enriched with hidden data, which can subsequently be decoded by the receiver. We investigate a hybrid strategy made of signal processing and RNN (Recurrent Neural Networks) to calculate and compress cepstral coefficients: these are descriptors of the speech signal, which can be embedded in the signal itself and used at the receiver's end to perform an Informed Speech Enhancement. Objective evaluations showed an increase in speech quality for noisy signals enhanced with our method.
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