The influence of stop consonants’ perceptual features on the Articulation Index model

Abstract

Studies on consonant perception under noise conditions typically describe the average consonant error as exponential in the Articulation Index (AI). While this AI formula nicely fits the average error over all consonants, it does not fit the error for any consonant at the utterance level. This study analyzes the error patterns of six stop consonants /p, t, k, b, d, g/ with four vowels (/ɑ/, /ɛ/, /I/, /ae/), at the individual consonant (i.e., utterance) level. The findings include that the utterance error is essentially zero for signal to noise ratios (SNRs) at least −2 dB, for >78% of the stop consonant utterances. For these utterances, the error is essentially a step function in the SNR at the utterance’s detection threshold. This binary error dependence is consistent with the audibility of a single binary defining acoustic feature, having zero error above the feature’s detection threshold. Also 11% of the sounds have high error, defined as ≥20% for SNRs greater than or equal to −2 dB. A grand average across many such sounds, having a natural distribution in thresholds, results in the error being exponential in the AI measure, as observed. A detailed analysis of the variance from the AI error is provided along with a Bernoulli-trials analysis of the statistical significance.