Predicting consonant recognition and confusions in normal-hearing listeners

Abstract

The perception of consonants in background noise has been investigated in various studies and was shown to critically depend on fine details in the stimuli. In this study, a microscopic speech perception model is proposed that represents an extension of the auditory signal processing model by Dau, Kollmeier, and Kohlrausch [(1997). J. Acoust. Soc. Am. 102, 2892-2905]. The model was evaluated based on the extensive consonant perception data set provided by Zaar and Dau [(2015). J. Acoust. Soc. Am. 138, 1253-1267], which was obtained with normal-hearing listeners using 15 consonant-vowel combinations mixed with white noise. Accurate predictions of the consonant recognition scores were obtained across a large range of signal-to-noise ratios. Furthermore, the model yielded convincing predictions of the consonant confusion scores, such that the predicted errors were clustered in perceptually plausible confusion groups. The large predictive power of the proposed model suggests that adaptive processes in the auditory preprocessing in combination with a cross-correlation based template-matching back end can account for some of the processes underlying consonant perception in normal-hearing listeners. The proposed model may provide a valuable framework, e.g., for investigating the effects of hearing impairment and hearing-aid signal processing on phoneme recognition.

Predicting effects of hearing-instrument signal processing on consonant perception

“…The consonant perception model of Zaar and Dau (2017) was considered for predicting the perceptual data obtained with the HA-processed CVs as well as with the CIprocessed VCVs. Figure 1 shows the model, which combines the auditory model front end of Dau et al (1996Dau et al ( , 1997 with a temporally dynamic correlation-based back end.…”

“…1. Scheme of the consonant perception model (reprinted from Zaar and Dau, 2017). For the test signal and a set of templates, the noisy speech and the noise alone were passed separately through the auditory model, consisting of a gammatone filterbank, an envelope extraction stage, a chain of adaptation loops, and a modulation filterbank.…”

“…After obtaining the correlation coefficients between each test signal and all templates, the internal noise was added and the model response for each iteration was defined as the template showing the largest correlation with the test signal. As proposed in Zaar and Dau (2017), the model was calibrated by adjusting the variance of the internal noise based on the average consonant recognition scores obtained in the considered conditions. Here, a variance of r 2…”

“…A model that can account for such effects therefore needs to be sensitive not only to the presence of a consonant cue, but also to its perceptual similarity with other consonant cues. Zaar and Dau (2017) proposed a consonant perception model that appears to provide such sensitivity. It combines an auditory model (Dau et al, 1996(Dau et al, , 1997) that includes adaptive processes and modulation-frequency selective processing with a temporally dynamic correlation-based template-matching back end.…”

“…In the present study, the effects of several HA and CI processing conditions on consonant perception were investigated using the model of Zaar and Dau (2017) to predict the behavioral data. In particular, an experimental investigation of the effects of HA processing (NLFC and INS) on NH listeners' consonant perception was conducted using speech material from Schmitt et al (2016).…”