Gunnar Fant developed acoustic models connecting the physical production of speech to its measurable signal. His Acoustic Theory of Speech Production by Gunnar Fant relates vocal-tract geometry to acoustic behavior, including calculations based on X-ray studies of articulations. The original book dates to 1960; the inspected publisher preview is its 1970 second printing.^[ref]

His source-filter model of speech production separates excitation from the filtering of the vocal tract. Changing a filter's resonances can move formants without requiring the same change in the fundamental frequency of the source. This makes speech synthesis an experimental tool: a researcher can vary model parameters and compare the generated signal with recorded speech. KTH's account explicitly describes his synthesis work as a means of testing production hypotheses.^[ref]

KTH's curriculum vitae records 1919–2009 and his leadership of the Speech Transmission Laboratory from 1951 to 1966.^[ref]

David Weenink co-created Praat with Paul Boersma, providing tools for measuring and modeling speech. His 2006 thesis investigated speaker-adaptive vowel identification: how recognition can remain useful when acoustic patterns vary between speakers.^[ref]^[ref]

The thesis describes recordings from 30 speakers, each producing 12 Dutch vowels in controlled sentence frames. It relates these data to formants, principal component analysis, discriminant analysis and adaptive classification methods. This makes the recognition problem experimentally concrete: a classifier must separate vowel identity from speaker-related variation rather than memorize a single speaker's frequency spectrum.^[ref]

. Portrait linked directly from David Weenink's official university-hosted homepage.

Work by David Weenink

Paul Boersma

Paul Boersma is a phonetician who co-created Praat with David Weenink. His work connects acoustic measurements to computational methods, including the estimation of fundamental frequency from sampled speech.^[ref]

His 1993 periodicity-analysis paper examines how signal sampling and a finite analysis window affect estimates based on autocorrelation. Repetition produces peaks at nonzero time lags; locating a suitable peak gives a candidate period, while the relative peak height contains information about periodicity. Correcting the influence of the analysis window is important before interpreting those peaks as properties of the original signal.^[ref]