Split matrix quantization with split vector quantization error compensation and selective enhanced processing for robust speech recognition
US6067515A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Oct 27, 1997 |
| Grant date | May 23, 2000 |
| Priority date | — |
| Expiry date | Oct 27, 2017 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A speech recognition system utilizes both split matrix and split vector quantizers as front ends to a second stage speech classifier such as hidden Markov models (HMMs) to, for example, efficiently utilize processing resources and improve speech recognition performance. Fuzzy split matrix quantization (FSMQ) exploits the "evolution" of the speech short-term spectral envelopes as well as frequency domain information, and fuzzy split vector quantization (FSVQ) primarily operates on frequency domain information. Time domain information may be substantially limited which may introduce error into the matrix quantization, and the FSVQ may provide error compensation. Additionally, acoustic noise influence may affect particular frequency domain subbands. This system also, for example, exploits the localized noise by efficiently allocating enhanced processing technology to target noise-affected input signal parameters and minimize noise influence. The enhanced processing technology includes a weighted LSP and signal energy related distance measure in training Linde-Buzo-Gray (LBG) algorithm and during recognition. Multiple codebooks may also be combined to form single respective codebooks f…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.