Cross-spectral based formant estimation and alignment

We develop new algorithms which address the problems of accurate frequency estimation and the comparison of the frequency content of two different spectra. We apply these algorithms to speech to estimate formants accurately from very little data and to estimate the frequency differences of individual formants of similar vowel instances. The first algorithm is a simple method for the construction of a very high-resolution, high-gain spectrum. This phase-reparameterized spectrum is based on a deterministic signal model and uses both Fourier phase and magnitude and accounts for all of the energy in the Fourier spectrum. To compare the formants of similar vowels, we introduce a spectral correlation algorithm based on an outer product rather than the inner product of conventional correlation.