Linear Prediction of Speech

By J. D. Markel, A. H. Gray. 1976

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Wavenet Based Low Rate Speech Coding (2017, methodology): “lmost always encode a similar set of parameters: spectral envelope, pitch, and voicing level. The parameter set differs little for approaches based on a temporal perspective with glottal pulse trains [15, 16], and those based on a frequencydomain perspective with sinusoids [17, 18]. Any of these par”
Unpacking “Honesty”: Vertebrate Vocal Production and the Evolution of Acoustic Signals (2003, unknown): “Second, he measured formant frequencies using a spectral estimation algorithm called linear prediction, which finds the optimal all-pole (all formant) model to fit a particular spectrum (Markel and Gray 1976).”
Hidden Markov Models for Speech Recognition (1991, methodology): “These include such standard methods as measurement of the discrete (fast) Fourier transform (FFT), all-pole minimum-phase linear prediction (LPC) methods, and autoregressive/moving average models (Allen and Rabiner 1977; Atal and Hanauer 1971; Cadzow 1982; Makhoul 1975; Markel and Gray 1976; Schaf”
A new model of LPC excitation for producing natural-sounding speech at low bit rates (1982, background): “However, 'the model for the excitation of the filter was still carried over from the channel vocoders [ 3 ].”