A narrow band pattern-matching model of vowel perception

The purpose of this paper is to propose and evaluate a new model of vowel perception which assumes that vowel identity is recognized by a template-matching process involving the comparison of narrow band input spectra with a set of smoothed spectral-shape templates that are learned through ordinary exposure to speech. In the present simulation of this process, the input spectra are computed over a sufficiently long window to resolve individual harmonics of voiced speech. Prior to template creation and pattern matching, the narrow band spectra are amplitude equalized by a spectrum-level normalization process, and the information-bearing spectral peaks are enhanced by a "flooring" procedure that zeroes out spectral values below a threshold function consisting of a center-weighted running average of spectral amplitudes. Templates for each vowel category are created simply by averaging the narrow band spectra of like vowels spoken by a panel of talkers. In the present implementation, separate templates are used for men, women, and children. The pattern matching is implemented with a simple city-block distance measure given by the sum of the channel-by-channel differences between the narrow band input spectrum (level-equalized and floored) and each vowel template. Spectral movement is taken into account by computing the distance measure at several points throughout the course of the vowel. The input spectrum is assigned to the vowel template that results in the smallest difference accumulated over the sequence of spectral slices. The model was evaluated using a large database consisting of 12 vowels in /hVd/ context spoken by 45 men, 48 women, and 46 children. The narrow band model classified vowels in this database with a degree of accuracy (91.4%) approaching that of human listeners.