Real-time spectrographic analysis involves processing of spectral outputs as vectors in multidimensional space. To reduce the number of dimensions and eliminate redundancy between channels, a covariance matrix can be derived and the eigenvectors calculated. Correlating the spectral cross sections with as few as three eigenvectors can account for up to 98% of the total variance. Following the theory of Yilmas [H. Yilmas, NASA Contract 12–129, Final Rept. (Dec. 1966)], a real-time display using correlations of the first three eigenvectors has been written for a PDP-1 computer. The display consists of a circle representing a cross section of a cone, and a vector representing cone height. The height represents loudness, as determined by the correlation of the first eigenvector; and the circle is analogous to a vowel circle derived from Eigenvectors 2 and 3, normalized for volume by Eigenvector 1. The display provides effective phoneme separation: display coordinates are useful for automatic phoneme identification. The eigenvectors can be quickly recalculated to accommodate variations in language or speech type.
Automatic word-recognition systems typically exhibit marked degradation in performance when used on speakers other than those who contributed the “learn” data. Extension of word-recognition systems to accommodate new speakers usually involves extensive retraining. A procedure which would measure and “adapt” to characteristics of a new speaker would allow for improved recognition rates without retraining. One such procedure has been to measure maxima and minima of formant frequencies and adjust the recognition accordingly [L. J. Gerstman, “Classification of Self-Normalized Vowels,” AFCRL Conference on Speech Communication and Processing Reprints (1967)]. Of particular significance are procedures which involve little or no manual intervention and allow the recognition to remain “automatic.” This paper reports on several approaches, including use of the sample covariance matrix as a purely statistical method, and use of a “phoneme-map” as a more subjective manual intervention method. The various approaches are discussed in terms of degree of recognition improvement, amount of additional complexity, and amount of nonautomatic effort required.
In the course of testing a part of the vowel perception model proposed by Huseyin Yilmaz, a tape was prepared in which the spectrum was distorted in certain ways. The effect is sufficiently striking to warrant exposure, although the theory is not completely tested. For the demonstration, a vowel is filtered in such a way that in isolation it sounds like another vowel. When this acoustic event is imbedded in a sentence all of which is similarly filtered, it is perceived as the original vowel. This effect supports a relativistic theory of vowel perception.
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