# US 8,788,270 B2 · Apparatus and method for determining an emotion state of a speaker > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,788,270 B2 - Document ID: US8788270B2 - Status: Granted - Kind: B2 - Application number: 13/377,801 - Filed: 2010-06-16 - Published: 2012-04-12 - Granted: 2014-07-22 - Earliest claimed: 2009-06-16 - Assignees: University of Florida Research Foundation, Inc. - Applicants: Sona Patel; Rahul Shrivastav - Inventors: Sona Patel; Rahul Shrivastav - Web page: https://patentgrep.com/patent/US8788270B2 ## CPC - G10L 25/00: Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 (muting semiconductor-based amplifiers when some special characteristics of a signal are sensed by a speech detector, e.g. sensing when no signal is present, H03G3/34) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract A method and apparatus for analyzing speech are provided. A method and apparatus for determining an emotion state of a speaker are provided, including providing an acoustic space having one or more dimensions, where each dimension corresponds to at least one baseline acoustic characteristic; receiving an utterance of speech by the speaker; measuring one or more acoustic characteristics of the utterance; comparing each of the measured acoustic characteristics to a corresponding baseline acoustic characteristic; and determining an emotion state of the speaker based on the comparison. An embodiment involves determining the emotion state of the speaker within one day of receiving the subject utterance of speech. An embodiment involves determining the emotion state of the speaker, where the emotion state of the speaker includes at least one magnitude along a corresponding at least one of the one or more dimensions within the acoustic space. ## Claims ### Claim 1 A method for determining an emotion state of a speaker, comprising: providing an acoustic space having one or more dimensions, wherein each dimension of the one or more dimensions of the acoustic space corresponds to at least one baseline acoustic characteristic; receiving a subject utterance of speech by a speaker; measuring, via one or more processors, one or more acoustic characteristics of the subject utterance of speech; comparing, via the one or more processors, each acoustic characteristic of the one or more acoustic characteristics of the subject utterance of speech to a corresponding one or more baseline acoustic characteristic; and determining, via the one or more processors, an emotion state of the speaker based on the comparison, wherein determining the emotion state of the speaker based on the comparison occurs within one day of receiving the subject utterance of speech by the speaker. ### Claim 2 (depends on 1) The method according to claim 1, wherein providing an acoustic space comprises analyzing training data to determine the at least one baseline acoustic characteristic for each of the one or more dimensions of the acoustic space. ### Claim 3 (depends on 1) The method according to claim 1, wherein determining the emotion state of speaker based on the comparison comprises determining one or more emotions of the speaker based on the comparison. ### Claim 4 (depends on 1) The method according to claim 1, wherein the emotion state of the speaker comprises a category of emotion and an intensity of the category of emotion. ### Claim 5 (depends on 1) The method according to claim 1, wherein the emotion state of the speaker comprises at least one magnitude along a corresponding at least one of the one or more dimensions within the space. ### Claim 6 (depends on 1) The method according to claim 1, wherein each of the at least one baseline acoustic characteristic for each dimension of the one or more dimensions affects perception of the emotion state. ### Claim 7 (depends on 2) The method according to claim 2, wherein the training data comprises at least one training utterance of speech. ### Claim 8 (depends on 7) The method according to claim 7, wherein the at least one training utterance of speech comprises at least two training utterances of speech. ### Claim 9 (depends on 7) The method according to claim 7, wherein one or more of the at least one training utterance of speech is spoken by the speaker. ### Claim 10 (depends on 7) The method according to claim 7, wherein one or more of the at least one training utterance of speech is spoken by an additional speaker. ### Claim 11 (depends on 7) The method according to claim 7, wherein the subject utterance of speech comprises one or more of the at least one training utterance of speech. ### Claim 12 (depends on 11) The method according to claim 11, wherein semantic and/or syntactic content of the one or more of the at least one training utterance of speech is determined by the speaker. ### Claim 13 (depends on 1) The method according to claim 1, wherein the subject utterance of speech comprises a 2 to 10 second segment of speech. ### Claim 14 (depends on 1) The method according to claim 1, further comprising selecting a segment of speech from the subject utterance of speech, wherein measuring the one or more acoustic characteristics of the subject utterance of speech comprises measuring one or more acoustic characteristic of the segment of speech. ### Claim 15 (depends on 14) The method according to claim 14, wherein the segment of speech from the subject utterance of speech is a 2 to 10 second segment of speech from the subject utterance of speech. ### Claim 16 (depends on 15) The method according to claim 15, wherein the segment of speech from the subject utterance of speech is a 3 to 5 second segment of speech from the subject utterance of speech. ### Claim 17 (depends on 14) The method according to claim 14, further comprising: selecting an additional segment of speech from the subject utterance of speech; measuring one or more additional acoustic characteristics of the additional segment of speech, wherein each one or more additional acoustic characteristic of the additional segment of speech corresponds to a corresponding one or more baseline acoustic characteristic; comparing each one or more additional acoustic characteristic of the additional segment of speech to the corresponding one or more baseline acoustic characteristic; and determining an additional emotion state of the speaker based on the comparison. ### Claim 18 (depends on 17) The method according to claim 17, wherein the segment of speech from the subject utterance of speech and the additional segment of speech from the subject utterance of speech are of different lengths. ### Claim 19 (depends on 1) The method according to claim 1, wherein at least one of the one or more acoustic characteristic of the subject utterance of speech comprises a suprasegmental property of the subject utterance of speech, and corresponding at least one of the one or more baseline acoustic characteristic comprises a corresponding suprasegmental property. ### Claim 20 (depends on 1) The method according to claim 1, wherein each of the one or more acoustic characteristic of the subject utterance of speech is selected from the group consisting of: fundamental frequency, pitch, intensity, loudness, and speaking rate. ### Claim 21 (depends on 1) The method according to claim 1, wherein each of the one or more acoustic characteristic of the subject utterance of speech is selected from the group consisting of: number of peaks in the pitch, intensity contour, loudness contour, pitch contour, fundamental frequency contour, attack of the intensity contour, attack of the loudness contour, attack of the pitch contour, attack of the fundamental frequency contour, fall of the intensity contour, fall of the loudness contour, fall of the pitch contour, fall of the fundamental frequency contour, duty cycle of the peaks in the pitch, normalized minimum pitch, normalized maximum of pitch, cepstral peak prominence (CPP), and spectral slope. ### Claim 22 (depends on 1) The method according to claim 1, wherein determining the emotion state of the speaker based on the comparison occurs within one minute of receiving the subject utterance of speech by the speaker. ### Claim 23 (depends on 1) The method according to claim 1, wherein determining the emotion state of the speaker based on the comparison occurs within 30 seconds of receiving the subject utterance of speech by the speaker. ### Claim 24 (depends on 1) The method according to claim 1, wherein determining the emotion state of the speaker based on the comparison occurs within 15 seconds of receiving the subject utterance of speech by the speaker. ### Claim 25 (depends on 1) The method according to claim 1, wherein determining the emotion state of the speaker based on the comparison occurs within 10 seconds of receiving the subject utterance of speech by the speaker. ### Claim 26 (depends on 1) The method according to claim 1, wherein determining the emotion state of the speaker based on the comparison occurs within 5 seconds of receiving the subject utterance of speech by the speaker. ### Claim 27 A method for determining an emotion state of a speaker, comprising: providing an acoustic space having one or more dimensions, wherein each dimension of the one or more dimensions of the acoustic space corresponds to at least one baseline acoustic characteristic; receiving a subject utterance of speech by a speaker; measuring, via one or more processors, one or more acoustic characteristic of the subject utterance of speech; comparing, via the one or more processors, each acoustic characteristic of the one or more acoustic characteristic of the subject utterance of speech to a corresponding one or more baseline acoustic characteristic; and determining, via the one or more processors, an emotion state of the speaker based on the comparison, wherein the emotion state of the speaker comprises at least one magnitude along a corresponding at least one of the one or more dimensions within the acoustic space. ### Claim 28 (depends on 27) The method according to claim 27, wherein each of the at least one baseline acoustic characteristic for each dimension of the one or more dimensions affects perception of the emotion state. ### Claim 29 (depends on 27) The method according to claim 27, wherein the one or more dimensions is one dimension. ### Claim 30 (depends on 27) The method according to claim 27, wherein the one or more dimensions is two or more dimensions. ### Claim 31 (depends on 27) The method according to claim 27, wherein providing an acoustic space comprises analyzing training data to determine the at least one baseline acoustic characteristic for each of the one or more dimensions of the acoustic space. ### Claim 32 (depends on 31) The method according to claim 31, wherein the acoustic space describes n emotions using n−1 dimensions, where n is an integer greater than 1. ### Claim 33 (depends on 32) The method according to claim 32, further comprising reducing the n−1 dimensions to p dimensions, where p