Modeling the judgment of vowel quality differences

Abstract

The hypothesis of this study is that the auditory cues relevant to listeners' judgment of vowel quality are a spectral representation of loudness density versus pitch. A model is described that generates such patterns for steady-state vowels. In addition to the nonlinear transformations underlying the loudness density and pitch scales, it incorporates experimentally established characteristics associated with frequency resolution and masking, such as the critical band concept. This model is combined with a measure of auditory perceptual distance which, operating on pairs of vowels, treats each stimulus representation as a single spectral shape. In order to test the distance metric and the model, experimental data were gathered from listeners' numerical estimates of quality differences between stimulus pairs which compared four-formant and two-formant vowels. The correlation between experimental and theoretical results was 0.89. We interpret this value to indicate that the present definition of auditory cue and auditory distance can be said to account for the experimental behavior of our listeners only in a rather gross fashion. On the other hand, the theory was developed on the basis of rather conservative assumptions about the nature of auditory cues. For instance, the model ignores the possibility of temporal coding and certain nonlinear effects, and it does not pay special attention to spectral peaks. Seen in that light, the agreement between observed and predicted auditory distance is remarkably good.

The role of f 0 and formant frequencies in distinguishing the voices of men and women

“…Briefly, these problems include the following: (1) the determinacy problem, as Bladon (1982) has called it, which is the commonplace idea that tracking formants in natural speech is a difficult and, as yet, unresolved problem; (2) the straightforward observation that perceptual confusions made by human listeners nearly always involve speech sounds that are phonetically quite similar, a pattern that is difficult to reconcile with an underlying formant tracking process that is susceptible to gross errors that occur when formants either split or merge 4 (Klatt, 1982; see also Ito, Tsuchida, & Yano, 2001); (3) evidence showing that spectral details other than formant frequencies can affect phonetic quality (e.g., Bladon, 1982;Chistovich & Lublinskaja, 1979;Hillenbrand & Nearey, 1999). Partially in response to these problems, a number of investigators have argued that phonetic recognition is mediated by mental computations of similarities and differences in the gross shape of the spectrum rather than by formant frequencies (e.g., Bladon & Lindblom, 1981;Hillenbrand & Houde, 2003;Zahorian & Jagharghi, 1993). The issues discussed above revolve around the question of formants versus gross spectral shape in the perception of phonetic quality rather than talker sex, but the same question can be asked about either issue.…”

The Use of Artificial Neural Networks in the estimation of the Perception of Sound By the Human Auditory System

“…4, where S is the resulting perceived loudness in Sone and l is the loudness level in Phon. [12] III. ARTIFICIAL NEURAL NETWORKS A biological neural network is an interconnection of processing elements (neurons) responsible for the processing of information in the nervous systems of animals.…”

“…The algorithm for this conversion is given in Eq. 4 earlier in this chapter [12].The Sone scale of perceived loudness is often thought to be a more accurate representation of the manner in which any continuous functional mapping" [16].The continuity of a function has many different levels. C0 continuity denotes that the function is continuous and it does not generate any discrete behavior.…”

Exploring Music Collections by Browsing Different Views

“…The main characteristic is that lower frequencies have a stronger masking influence on higher frequencies than vice versa. The contribution of critical-band z i to z j with ∆z = z j − z i is attenuated by, We calculate the loudness in sone using the formula suggested by Bladon and Lindblom (1981),…”