School of Human Develop., Univ. of Texas, Box 830688, GR 41, Richardson, TX 75083
When two synthetic vowels are presented concurrently, listeners identify the vowels more accurately if they differ in fundamental frequency (F[sub 0]) or if one of them is preceded/followed by a gliding (versus static) formant pattern. Previous experiments have shown that gliding formants generally do not help listeners identify the vowel to which they are linked; instead, they make the vowel without transitions easier to identify. One explanation is that the formant transition region provides a brief interval during which the competing steady-state vowel is perceptually more prominent. This interpretation is supported by two computational models that perform a filter bank analysis, process the waveform in each filter channel using a sliding temporal window, and determine which region of the signal provides the strongest evidence of each vowel. Model A computed the energy in each channel at successive time intervals to generate running excitation patterns. Model B used a temporal analysis to generate running autocorrelation functions, and included a further stage to partition the channels based on periodicity cues. Both models predicted effects of F[sub 0] and gliding formants, but model B provided better predictions of the pattern of listeners' identification responses. Identification of concurrent vowels appears to benefit from an analysis of the composite waveform using a sliding temporal window, combined with a form of F[sub 0]-guided source segregation.