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.

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The new AT&T Text-To-Speech (TTS) system for general U.S. English text is based on best-choice components of the AT&T Flextalk TTS, the Festival System from the University of Edinburgh, and ATR's CHATR system. From Flextalk, it employs text normalization, letter-to-sound, and prosody generation. Festival provides a flexible and modular architecture for easy experimentation and competitive evaluation of different algorithms or modules. In addition, we adopted CHATR's unit selection algorithms and modified them in an attempt to guarantee high intelligibility under all circumstances. Finally, we have added our own Harmonic plus Noise Model (HNM) backend for synthesizing the output speech. Most decisions made during the research and development phase of this system were based on formal subjective evaluations. We feel that the new system goes a long way toward delivering on the long-standing promise of truly natural-sounding, as well as highly intelligible, synthesis.