Wavelet-based energy binning cepstal features for automatic speech recognition
US6253175A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Nov 30, 1998 |
| Grant date | Jun 26, 2001 |
| Priority date | — |
| Expiry date | Nov 30, 2018 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/0631
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for processing acoustic speech signals which utilize the wavelet transform (and alternatively, the Fourier transform) as a fundamental tool. The method essentially involves "synchrosqueezing" spectral component data obtained by performing a wavelet transform (or Fourier transform) on digitized speech signals. In one aspect, spectral components of the synchrosqueezed plane are dynamically tracked via a K-means clustering algorithm. The amplitude, frequency and bandwidth of each of the components are, thus, extracted. The cepstrum generated from this information is referred to as "K-mean Wastrum." In another aspect, the result of the K-mean clustering process is further processed to limit the set of primary components to formants. The resulting features are referred to as "formant-based wastrum." Formants are interpolated in unvoiced regions and the contribution of unvoiced turbulent part of the spectrum are added. This method requires adequate formant tracking. The resulting robust formant extraction has a number of applications in speech processing and analysis including vocal tract normalization.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.