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-the-art NELE algorithms, employing either noise-adaptive or non-adaptive strategies, and with or without compensation for reverberation.