# US 4,394,538 A · Speech recognition system and method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 4,394,538 A - Document ID: US4394538A - Status: Granted - Kind: A - Application number: 06/240,273 - Filed: 1981-03-04 - Granted: 1983-07-19 - Earliest claimed: 1981-03-04 - Assignees: Threshold Technology, Inc. - Inventors: Ben Warren; George M. White; Rick Parfitt; Peter Deng - Web page: https://patentgrep.com/patent/US4394538A ## CPC - G10L 15/063: {Training} - G10L 15/00: Speech recognition (G10L17/00 takes precedence) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract An apparatus for receiving spoken input training words and for subsequently recognizing a spoken input command word. For each training word, and for the command word, there is stored a time dependent array and a cumulative feature non-time-dependent vector. The command word array is compared, member-by-member, with each training word array. Also, the command word vector is compared, feature-by-feature, with each training word vector. The results of both comparisons are used for selecting the training word which most closely matches the command word. ## Claims ### Claim 1 Apparatus for receiving spoken input training words and for subsequently recognizing a spoken input command word, comprising: feature extraction means for generating feature signals indicative of the presence or absence of features in a spoken input word; means for storing, as a time-dependent reference array, the presence or absence status of feature signals during each training word; means for storing, as a non-time-dependent reference vector, an indication of the cumulative presence of feature signals during each training word; means for storing, as a time-dependent feature array, the status of feature signals during the command word; means for storing, as a non-time-dependent feature vector, an indication of the cumulative presence of feature signals during the command word; means for comparing member-by-member, the command word feature array with each training word reference array to obtain an array comparison figure for each training word; means for comparing, feature-by-feature, the command word feature vector with each training word reference vector to obtain a vector comparison figure for each training word; and means responsive to both the array comparison figure and the vector comparison figure for each training word for selecting the training word which most closely matches the command word. ### Claim 2 (depends on 1) Apparatus as defined by claim 1 wherein said means for selecting said training word is responsive to a weighted sum of said array comparison figure and said vector comparison figure. ### Claim 3 (depends on 1) Apparatus as defined by claim 1 wherein said non-time-dependent reference vectors and feature vectors are representative of the normalized cumulative fractions of the spoken word duration during which individual feature components are present. ### Claim 4 (depends on 2) Apparatus as defined by claim 2 wherein said non-time-dependent reference vectors and feature vectors are representative of the normalized cumulative fractions of the spoken word duration during which individual feature components are present. ### Claim 5 (depends on 1) Apparatus as defined by claim 1 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU2## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 6 (depends on 2) Apparatus as defined by claim 2 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU3## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 7 (depends on 4) Apparatus as defined by claim 4 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU4## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 8 Apparatus for receiving spoken input training words and for subsequently recognizing a spoken input command word and generating an occurrence indication indicative of said command word, said occurrence indication being suitable for control of a companion system, comprising: feature extraction means for generating feature signals indicative of the presence or absence of features in a spoken input word; means for storing, as a time-dependent reference array, the presence or absence status of feature signals during each training word; means for storing, as a non-time-dependent reference vector, an indication of the cumulative presence of feature signals during each training word; means for storing, as a time-dependent feature array, the status of feature signals during the command word; means for storing, as a non-time-dependent feature vector, an indication of the cumulative presence of feature signals during the command word; means for comparing member-by-member, the command word feature array with each training word reference array to obtain an array comparison figure for each training word; means for comparing, feature-by-feature, the command word feature vector with each training word reference vector to obtain a vector comparison figure for each training word; and means responsive to both the array comparison figure and the vector comparison figure for each training word for selecting the training word which most closely matches the command word and for generating an occurrence indication that represents the selected training word. ### Claim 9 (depends on 8) Apparatus as defined by claim 8 wherein said means for selecting said training word is responsive to a weighted sum of said array comparison figure and said vector comparison figure. ### Claim 10 (depends on 8) Apparatus as defined by claim 8 wherein said non-time-dependent reference vectors and feature vectors are representative of the normalized cumulative fractions of the spoken word duration during which individual feature components are present. ### Claim 11 (depends on 9) Apparatus as defined by claim 9 wherein said non-time-dependent reference vectors and feature vectors are representative of the normalized cumulative fractions of the spoken word duration during which individual feature components are present. ### Claim 12 (depends on 8) Apparatus as defined by claim 8 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU5## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 13 (depends on 9) Apparatus as defined by claim 9 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU6## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 14 (depends on 11) Apparatus as defined by claim 11 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU7## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 15 (depends on 9) Apparatus as defined by claim 9 wherein said occurrence indication is generated only when the combination of said array comparison figure and said vector comparison figure meets a predetermined standard. ### Claim 16 (depends on 10) Apparatus as defined by claim 10 wherein said occurrence indication is generated only when the combination of said array comparison figure and said vector comparison figure meets a predetermined standard. ### Claim 17 (depends on 11) Apparatus as defined by claim 11 wherein said occurrence indication is generated only when the combination of said array comparison figure and said vector comparison figure meets a predetermined standard. ### Claim 18 A method for receiving spoken input training words and for subsequently recognizng a spoken input command word, comprising the steps of: generating feature signals indicative of the presence or absence of features in a spoken input word; storing, as a time-dependent reference array, the presence or absence status of feature signals during each training word; storing, as a non-time-dependent reference vector, an indication of the cumulative presence of feature signals during each training word; storing, as a time-dependent feature array, the status of feature signals during the command word; storing, as a non-time-dependent feature vector, an indication of the cumulative presence of feature signals during the command word; comparing member-by-member, the command word feature array with each training word reference array to obtain an array comparison figure for each training word; comparing, feature-by-feature, the command word feature vector with each training word reference vector to obtain a vector comparison figure for each training word; and selecting, as a function of both the array comparison figure and the vector comparison figure, the training word which most closely matches the command word. ### Claim 19 (depends on 18) The method as defined by claim 18 wherein said training word is selected as a function of a weighted sum of said array comparison figure and said vector comparison figure. ### Claim 20 (depends on 18) The method as defined by claim 18 wherein said non-time-dependent reference vectors and feature vectors are representative of the normalized cumulative fractions of the spoken word duration during which individual feature components are present. ### Claim 21 (depends on 19) The method as defined by claim 19 wherein said non-time-dependent reference vectors and feature vectors are representative of the normalized cumulative fractions of the spoken word duration during which individual feature components are present. ### Claim 22 (depends on 18) The method as defined by claim 18 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU8## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 23 (depends on 19) The method as defined by claim 19 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU9## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i are the individual vector components for the command word. ### Claim 24 (depends on 21) The method as defined by claim 21 wherein said vector comparison figure SV.sub.m for each training word m is determined as being ##EQU10## where n is the number of features in the vector, VR.sub.mi are the individual vector components for the training word m, and VF.sub.i re the individual vector components for the command word.