Consonant-in-noise discrimination using an auditory model with different speech- based decision devices
* Presenting author
Abstract:
This study presents insights into the discrimination of two consonants presented in vowel-consonant-vowel (VCV) words embedded in speech-shaped noise by adopting an auditory model that uses a modulation filter bank front-end followed by either of two speech back-end decision modules from the literature. These decision modules have been validated in the past for the discrimination of: (1) closed-sets of CVC and VCV words (back-end 1), and (2) open-set sentences (back-end 2). Our analysis is focused on the discrimination cues available to the model, evaluating whether these cues might be further used to simulate listener-dependent performance. For that purpose we will rely on a reverse-correlation approach by comparing the noise representations that lead to the choice of one or the other consonant.