# US 2008/0189109 A1 · Segmentation posterior based boundary point determination > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2008/0189109 A1 - Document ID: US20080189109A1 - Status: Published application - Kind: A1 - Application number: 11/702,373 - Filed: 2007-02-05 - Published: 2008-08-07 - Earliest claimed: 2007-02-05 - Assignees: Microsoft Corporation - Inventors: Yu Shi; Frank Kao-Ping Soong - Web page: https://patentgrep.com/patent/US20080189109A1 ## CPC - G10L 15/04: Segmentation; Word boundary detection - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract Boundary points for speech in an audio signal are determined based on posterior probabilities for the boundary points given a set of possible segmentations of the audio signal. The boundary point posterior probability is determined based on a set of level posterior probabilities that each provide the probability of a sequence of feature vectors given one of the segmentations in the set of possible segmentations. ## Claims ### Claim 1 A method comprising: performing multiple segmentations of a sequence of feature vectors that represent an audio signal, each segmentation providing at least one segment boundary that is different from segment boundaries in other segmentations; determining a separate segmentation probability for each segmentation, each segmentation probability providing the probability of the sequence of feature vectors given the segmentation; determining a boundary point posterior probability for a possible boundary point of speech by summing over the segmentation probabilities for a set of segmentations that segment the sequence of feature vectors such that the possible boundary point represents a boundary point for speech in the segmentation; and using the boundary point posterior probability to select a possible boundary point of speech as a boundary point of speech. ### Claim 2 (depends on 1) The method of claim 1 wherein determining a segmentation probability comprises determining a separate segment posterior probability for each segment in the segmentation, each segment posterior probability providing the probability of a sub-sequence of the sequence of feature vectors given the segment that the sub-sequence of feature vectors is segmented into in the segmentation. ### Claim 3 (depends on 2) The method of claim 2 wherein determining a segment posterior probability comprises determining a separate probability for each feature vector in the sub-sequence, wherein determining a probability for a feature vector comprises applying the feature vector to a normal distribution for a segment. ### Claim 4 (depends on 3) The method of claim 3 wherein the normal distribution comprises a mean that is computed from the feature vectors in the segment. ### Claim 5 (depends on 1) The method of claim 1 wherein the set of segmentations comprises levels of segmentation where each level comprises a different number of segments and wherein the number of segments is between a minimum level and a maximum level such that the set of levels contains fewer than all of the levels in which a segmentation was performed. ### Claim 6 (depends on 5) The method of claim 5 further comprising determining the maximum level by identifying a level of segmentation at which a penalized homogeneity score is minimized. ### Claim 7 (depends on 6) The method of claim 6 wherein the penalized homogeneity score comprises a distortion measure and a weighted penalty that is based on the number of segments in the level. ### Claim 8 (depends on 7) The method of claim 7 wherein the weighted penalty is weighted by a weight that is trained on training data to minimize the mean square error of the estimated maximum level. ### Claim 9 (depends on 8) The method of claim 8 wherein determining a boundary point posterior probability for a possible boundary point of speech comprises determining multiple boundary point posterior probabilities for multiple possible boundary points of speech. ### Claim 10 (depends on 9) The method of claim 9 wherein determining multiple boundary point posterior probabilities for multiple possible boundary points of speech comprises determining a boundary point posterior probability for at least one starting boundary point that represents the start of speech and determining a boundary point posterior probability for at least one ending boundary point that represents the end of speech. ### Claim 11 (depends on 10) The method of claim 10 wherein determining a boundary point posterior probability for a starting boundary point and a boundary point posterior probability for an ending boundary point comprises using a different maximum level for the starting boundary point than for the ending boundary point. ### Claim 12 A computer-readable medium having computer-executable instructions for performing steps comprising: selecting a boundary point for speech in an audio signal from a plurality of possible boundary points found in a plurality of possible segmentations of the audio signal; forming a summation of probabilities that is limited to probabilities associated with segmentations in the plurality of possible segmentations in which the selected possible boundary point is positioned as a boundary point; using the summation of probabilities to determine a probability that the selected possible boundary point is a boundary point for speech; and using the probability that the selected possible boundary point is a boundary point for speech to set a boundary point for speech in the audio signal. ### Claim 13 (depends on 12) The computer-readable medium of claim 12 wherein each possible segmentation in the plurality of possible segmentations has a different number of segments. ### Claim 14 (depends on 12) The computer-readable medium of claim 12 further comprising determining a maximum number of segments that can be in a segmentation associated with a probability used to form the summation of probabilities based on penalized homogeneity scores for the plurality of segmentations. ### Claim 15 (depends on 12) The computer-readable medium of claim 12 wherein a probability associated with a segmentation is determined based in part on a probability of a feature vector given a segment in the segmentation. ### Claim 16 (depends on 15) The computer-readable medium of claim 15 wherein the probability of a feature vector given a segment is based in part on a mean feature vector for the segment. ### Claim 17 A method comprising: determining a maximum number of segments that can be found in segmentations used to determine the probability of a possible boundary point of speech in an audio signal based in part on distortion measures for a plurality of segmentations; determining probabilities for a plurality of segmentations that have fewer than the maximum number of segments; using the probabilities for the plurality of segmentations to determine a probability for each of at least two boundary points; and selecting one of the at least two boundary points as a boundary point of speech in the audio signal based on the probabilities for the at least two boundary points. ### Claim 18 (depends on 17) The method of claim 17 wherein the plurality of segmentations comprise at least one segmentation with more than the maximum number of segments. ### Claim 19 (depends on 17) The method of claim 17 wherein determining a probability for a segmentation comprises determining a probability of a feature vector given a segment in the segmentation. ### Claim 20 (depends on 19) The method of claim 19 wherein the probability of a feature vector given a segment is based in part on a mean feature vector for the segment.