Matrix quantization with vector quantization error compensation and neural network postprocessing for robust speech recognition
US6347297B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 5, 1998 |
| Grant date | Feb 12, 2002 |
| Priority date | — |
| Expiry date | Oct 5, 2018 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/144
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A speech recognition system utilizes both matrix and vector quantizers as front ends to a second stage speech classifier such as hidden Markov models (HMMs) and utilizes neural network postprocessing to, for example, improve speech recognition performance. Matrix quantization exploits the “evolution” of the speech short-term spectral envelopes as well as frequency domain information, and vector quantization (VQ) primarily operates on frequency domain information. Time domain information may be substantially limited which may introduce error into the matrix quantization, and the VQ may provide error compensation. The matrix and vector quantizers may split spectral subbands to target selected frequencies for enhanced processing and may use fuzzy associations to develop fuzzy observation sequence data. A mixer provides a variety of input data to the neural network for classification determination. The neural network's ability to analyze the input data generally enhances recognition accuracy. Fuzzy operators may be utilized to reduce quantization error. Multiple codebooks may also be combined to form single respective codebooks for split matrix and split vector quantization…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.