Multi-dimensional phoneme identification models add noise scaled to individual perceptual acuity

Evidence

The model estimates relevant features from electrodograms generated by the CI sound processor. Because we are modelling a vowel identification task it makes sense to extract features related to formant frequencies. Moreover, because we are analyzing the effect of enhancing spectral contrast it seems logical to use spectral contrast features between formants. In this study the number of features was limited to two formants and therefore the MPI model is two dimensional. Next, the MPI model adds noise to the features. The variance of the noise is set based on the individual abilities of a CI user to perceive the features extracted from the electrodogram. In our implementation we used the results of the SMT task to set the noise variance. The obtained jnd from the SMT task was scaled between 0.001 and 0.5. This number was applied as the variance of the Gaussian noise applied in the MPI model and for this reason is termed jnd noise in Fig. 1 . — Dijk, Pim van, Physiology, Psychoacoustics and…, 1831 (#corroborates, via linked claim)

Categorization

• XREF: connects to: Speech discrimination requires five distinct auditory features — Professor Dr.-Ing. Hugo Fastl, Professor Dr.-Ing. Eberhard, Psychoacoustics- Facts and Models, 1868; Internal noise, not feature extraction, explains individual variation in model vowel identification; Vowel identification models inject noise scaled to each user’s spectral modulation threshold — Dijk, Pim van, Physiology, Psychoacoustics and…, 1831 (+1 more)

Related

• Vowel identification models inject noise scaled to each user’s spectral modulation threshold (similarity 0.95)
• Internal noise, not feature extraction, explains individual variation in model vowel identification (similarity 0.92)
• MPI vowel-identification models add Gaussian jnd noise scaled from SMT thresholds (similarity 0.88)
• Just-noticeable-difference noise, not map settings, drives vowel-identification variability between cochlear implant users (similarity 0.84)
• Subband and modulation power both strongly predict perceptual distances in consonant perception (similarity 0.81)
• Energy-based and modulation-based models predict consonant recognition but not confusions. (similarity 0.80)
• Increasing spectral contrast enhancement worsens vowel identification in CI users (similarity 0.80)
• Spectral modulation threshold declines track vowel identification declines (similarity 0.78)
• Frequency importance functions weight speech audibility by band significance (similarity 0.78)

Broader Connections

• Vowel identification models inject noise scaled to each user’s spectral modulation threshold #corroborates (0.95) — Dijk, Pim van, Physiology, Psychoacoustics and…, 1831
• Subband and modulation power both strongly predict perceptual distances in consonant perception #corroborates (0.72)