Biomedical subjects
Publications and source records attributed to H S Gopal.
Diplophonia has never been described in a neonate. An unusual case, then, of diplophonia in a neonate is described here. There is a description of the acoustic characteristics of this infant's cry and a discussion of their significance. This contributes to a review of the differential diagnosis of diplophonia. Moreover, this case further shows the essential relation between laryngoscopic findings and acoustical data.
The study of the acoustics of infants' vocalizations has important implications for the study of infant development and for clinical prediction. We suggest procedures that enhance the probability of discriminating among sub-glottal, periglottal, and supraglottal sites of pathology, With these procedures, then, it should become possible to contribute to diagnosis and therefore to treatment.
A quantitative perceptual model of human vowel recognition based upon psychoacoustic and speech perception data is described. At an intermediate auditory stage of processing, the specific bark difference level of the model represents the pattern of peripheral auditory excitation as the distance in critical bands (barks) between neighboring formants and between the fundamental frequency (F0) and first formant (F1). At a higher, phonetic stage of processing, represented by the critical bark difference level of the model, the transformed vowels may be dichotomously classified based on whether the difference between formants in each dimension falls within or exceeds the critical distance of 3 bark for the spectral center of gravity effect [Chistovich et al., Hear. Res. 1, 185-195 (1979)]. Vowel transformations and classifications correspond well to several major phonetic dimensions and features by which vowels are perceived and traditionally classified. The F1-F0 dimension represents vowel height, and high vowels have F1-F0 differences within 3 bark. The F3-F2 dimension corresponds to vowel place of articulation, and front vowels have F3-F2 differences of less than 3 bark. As an inherent, speaker-independent normalization procedure, the model provides excellent vowel clustering while it greatly reduces between-speaker variability. It offers robust normalization through feature classification because gross binary categorization allows for considerable acoustic variability. There was generally less formant and bark difference variability for closely spaced formants than for widely spaced formants. These findings agree with independently observed perceptual results and support Stevens' quantal theory of vowel production and perceptual constraints on production predicted from the critical bark difference level of the model.