Quantization using frequency and mean compensated frequency input data for robust speech recognition
Patent 6219642 Issued on April 17, 2001. Estimated Expiration Date: 
October 5, 2018. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.
October 5, 2018. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesSpeech recognition system using Markov models having independent label output sets Variable rate vocoder Vector quantization of a time sequential signal by quantizing an error between subframe and interpolated feature vectors Cepstral correction vector quantizer for speech recognition Speech recognition system and method which permits a speaker's utterance to be recognized using a hidden markov model with subsequent calculation reduction Speech recognition using equal division quantization Speech recognition apparatus Split matrix quantization with split vector quantization error compensation and selective enhanced processing for robust speech recognition Matrix quantization with vector quantization error compensation for robust speech recognition Patent #: 6070136 InventorsApplicationNo. 166648 filed on 10/05/1998US Classes:704/256, Markov704/243Creating patterns for matchingExaminersPrimary: Dorvil, RichemondAssistant: Armstrong, Angela Attorney, Agent or FirmInternational ClassesG10L 015/14G10L 015/08 AbstractA speech recognition system utilizes multiple quantizers to process frequency parameters and mean compensated frequency parameters derived from an input signal. The quantizers may be matrix and vector quantizer pairs, and such quantizer pairs may also function as front ends to a second stage speech classifiers such as hidden Markov models (HMMs) and/or utilizes neural network postprocessing to, for example, improve speech recognition performance. Mean compensating the frequency parameters can remove noise frequency components that remain approximately constant during the duration of the input signal. HMM initial state and state transition probabilities derived from common quantizer types and the same input signal may be consolidated to improve recognition system performance and efficiency. 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 may provide a variety of input data to the neural network for classification determination. 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 to reduce processing resources demand.Other References
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