# US 11,308,965 B2 · Voice information processing method and apparatus, and terminal

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- Number: US 11,308,965 B2
- Document ID: US11308965B2
- Status: Granted
- Kind: B2
- Application number: 16/754,540
- Filed: 2017-10-13
- Published: 2020-08-27
- Granted: 2022-04-19
- Earliest claimed: 2017-10-09
- Assignees: Huawei Technologies Co., Ltd.
- Applicants: Huawei Technologies Co., Ltd.
- Inventors: Zhicheng Sui; Yanming Li
- Web page: https://patentgrep.com/patent/US11308965B2

## CPC

- G10L 15/1822: {Parsing for meaning understanding}
- G10L 15/26: Speech to text systems (G10L15/08 takes precedence)
- G06F 40/30: Semantic analysis
- G10L 15/1815: {Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning}
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A voice information processing method includes: receiving, by a terminal, voice information, and converting the voice information into text information; obtaining a field probability that the text information belongs to each of preset M event fields; obtaining a prior probability that the text information belongs to each of N event fields, where N≤M; obtaining confidence that the text information belongs to each of the N event fields; calculating, based on the field probability, the prior probability, and the confidence that the text information belongs to each of the N event fields, values of N probabilities that the text information separately belongs to the N event fields; and outputting a semantic understanding result of performing semantic understanding based on an event field corresponding to a largest probability value in the N probability values.

## Claims

### Claim 1

A voice information processing method, implemented by a terminal, wherein the voice information processing method comprises:
receiving voice information;
performing semantic understanding on the voice information to convert the voice information into text information;
obtaining a field probability that the text information belongs to each of M event fields, wherein the M event field are preset in the terminal, and wherein the field probability represents a possibility that the text information belongs to one of the M event fields;
obtaining a prior probability that the text information belongs to each of N event fields, wherein the prior probability represents a probability that the text information belongs to one of the N event fields based on a plurality of times of performing the semantic understanding, wherein the N event fields in the M event fields, and wherein N is less than or equal to M;
obtaining confidence that the text information belongs to each of the N event fields, wherein the confidence represents a certainty degree that the text information belongs to one of the N event fields;
calculating, based on the field probability, the prior probability, and the confidence that the text information belongs to each of the N event fields, wherein values of N probabilities correspond to whether the text information belongs to the N event fields; and
outputting a semantic understanding result in response to performing the semantic understanding on the text information based on an event field of a largest probability value in the values of N probabilities.

### Claim 2 (depends on 1)

The voice information processing method of claim 1, wherein the N event fields are a plurality of first N event fields in the M event fields when N is less than M, wherein the first N event fields are in descending order of the field probabilities, and wherein N is greater than or equal to 2.

### Claim 3 (depends on 1)

The voice information processing method of claim 1, wherein after obtaining the field probability, the voice information processing method further comprises performing the semantic understanding on the text information in the N event fields to obtain N semantic understanding results.

### Claim 4 (depends on 1)

The voice information processing method of claim 1, wherein each of the M event fields corresponds to a keyword model, wherein the keyword model comprises a plurality of keywords of a corresponding event field, and wherein the voice information processing method further comprises:
performing segmentation processing on the text information;
extracting a segmented word;
obtaining information about distribution of one of the keywords of the segmented word in the keyword model of each of the M event fields; and
calculating the confidence that the text information belongs to each of the N event fields based on the distribution information.

### Claim 5 (depends on 1)

The voice information processing method of claim 1, further comprising:
performing segmentation processing on the text information;
extracting a segmented word;
searching a database model of each of the M event fields for a first feature of the segmented word, wherein the database model comprises a plurality of features, a weight of each of the features, and a segmented word of each of the features, wherein the weight indicates a probability that a second feature of the weight belongs to one of the M event fields of the database model, and wherein each of the M event fields corresponds to the database model; and
calculating the field probability that the text information belongs to each of the M event fields based on the weight of the second feature found in the database model.

### Claim 6 (depends on 1)

The voice information processing method of claim 1, wherein each of the M event fields corresponds to a keyword model, wherein the keyword model comprises a plurality of keywords and a probability, based on each of the keywords, indicating the text information belongs to one of the M event fields of the keyword model, wherein the voice information processing method further comprises:
recognizing at least one keyword from the text information;
obtaining a probability from the at least one keyword based on the keyword model of each of the M event fields; and
calculating the field probability that the text information belongs to each of the M event fields based on the probability from the at least one keyword.

### Claim 7 (depends on 1)

The voice information processing method of claim 1, further comprising performing an operation of the semantic understanding result after the terminal outputs the semantic understanding result.

### Claim 8

A terminal, comprising:
a processor; and
a memory coupled to the processor and storing instructions that, when executed by the processor, cause the terminal to be configured to:
receive voice information;
perform semantic understanding on the voice information to convert the voice information into text information;
obtain a field probability that the text information belongs to each of M event fields, wherein the M event fields are preset in the terminal, wherein the field probability represents a possibility that the text information belongs to one of the M event fields;
obtain a prior probability that the text information belongs to each of N event fields, wherein the prior probability represents a probability that the text information belongs to one of the M event fields based on a plurality of times the terminal performs the semantic understanding, wherein the N event fields are N of the M event fields, and wherein N is less than or equal to M;
obtain confidence that the text information belongs to each of the N event fields, wherein the confidence represents a certainty degree that the text information belongs to one of the N event fields;
calculate the prior probability and the confidence that the text information belongs to each of the N event fields based on the field probability, wherein values of N probabilities correspond to whether the text information belongs to the N event fields; and
output a semantic understanding result in response to performing the semantic understanding on the text information based on an event field of a largest probability value in the values of N probabilities.

### Claim 9 (depends on 8)

The terminal of claim 8, wherein the N event fields are a plurality of first N event fields in the M event fields when N is less than M, wherein the first N event fields are in descending order of the field probabilities, and wherein N is greater than or equal to 2.

### Claim 10 (depends on 8)

The terminal of claim 8, wherein the instructions further cause the terminal to be configured to separately perform the semantic understanding on the text information in the N event fields to obtain N semantic understanding results.

### Claim 11 (depends on 8)

The terminal of claim 8, wherein each of the M event fields corresponds to a keyword model, wherein the keyword model comprises a plurality of keywords of the corresponding event field, and wherein the instructions further cause the terminal to be configured to:
perform segmentation processing on the text information;
extract a segmented word;
obtain information about distribution of one of the keywords of the segmented word in the keyword model of each of the M event fields; and
calculate the confidence that the text information belongs to each of the N event fields based on the distribution information.

### Claim 12 (depends on 8)

The terminal of claim 8, wherein the instructions further cause the terminal to be configured to:
perform segmentation processing on the text information;
extract a segmented word;
search a database model of each of the M event fields for a first feature of the segmented word, wherein the database model comprises a plurality of features, a weight of each of the features, and a segmented word of each of the features, wherein the weight indicates a probability that a second feature of the weight belongs to one of the M event fields of the database model, and wherein each of the M event fields corresponds to the database model; and
calculate the field probability that the text information belongs to each of the M event fields based on the weight of the second feature found in the database model.

### Claim 13 (depends on 8)

The terminal of claim 8, wherein each of the M event fields corresponds to a keyword model, wherein the keyword model comprises a plurality of keywords and a probability, based on each of the keywords, indicating the text information belongs to one of the M event fields of the keyword model, and wherein the instructions further cause the terminal to be configured to:
recognize at least one keyword from the text information;
obtain a probability from the at least one keyword the keyword model of each of the M event fields; and
calculate the field probability that the text information belongs to each of the M event fields based on the probability from the at least one keyword.

### Claim 14 (depends on 8)

The terminal of claim 8, wherein the instructions further cause the terminal to be configured to perform an operation corresponding to the semantic understanding result after the terminal outputs the semantic understanding result.

### Claim 15

A computer program product comprising computer-executable instructions for storage on a non-transitory computer-readable medium that, when executed by a processor, cause a terminal to:
receive voice information;
perform semantic understanding on the voice information to convert the voice information into text information;
obtain a field probability that the text information belongs to each of M event fields, wherein the M event fields are preset in the terminal, and wherein the field probability represents a possibility that the text information belongs to one of the M event fields;
obtain a prior probability that the text information belongs to each of N event fields, wherein the prior probability represents a probability that the text information belongs to one of the N event fields based on a plurality of times of performing the semantic understanding, wherein the N event fields are N of the M event fields, and wherein N is less than or equal to M;
obtain confidence that the text information belongs to each of the N event fields, wherein the confidence represents a certainty degree that the text information belongs to one of the N event fields;
calculate, based on the field probability, the prior probability, and the confidence that the text information belongs to each of the N event fields, wherein values of N probabilities correspond to whether the text information belongs to the N event fields; and
output a semantic understanding result in response to performing the semantic understanding on the text information based on an event field of a largest probability value in the values of N probabilities.

### Claim 16 (depends on 15)

The computer program product of claim 15, wherein the N event fields are a plurality of first N event fields in the M event fields when N is less than M, wherein the first N event fields are in descending order of the field probabilities, and wherein N is greater than or equal to 2.

### Claim 17 (depends on 15)

The computer program product of claim 15, wherein the instructions further cause the terminal to separately perform the semantic understanding on the text information in the N event fields to obtain N semantic understanding results.

### Claim 18 (depends on 15)

The computer program product of claim 15, wherein each of the M event fields corresponds to a keyword model, wherein the keyword model comprises a plurality of keywords of the corresponding event field, and wherein the instructions further cause the terminal to:
perform segmentation processing on the text information;
extract a segmented word;
obtain information about distribution of one of the keywords of the segmented word in the keyword model of each of the M event fields; and
calculate the confidence that the text information belongs to each of the N event fields based on the distribution information.

### Claim 19 (depends on 15)

The computer program product of claim 15, wherein the instructions further cause the terminal to:
perform segmentation processing on the text information;
extract a segmented word;
search a database model of each of the M event fields for a first feature of the segmented word, wherein the database model comprises a plurality of features, a weight of each of the features, and a segmented word of each of the features, wherein the weight indicates a probability that a second feature of the weight belongs to one of the M event fields of the database model, and wherein each of the M event fields corresponds to the database model; and
calculate the field probability that the text information belongs to each of the M event fields based on the weight of the second feature found in the database model.

### Claim 20 (depends on 15)

The computer program product of claim 15, wherein each of the M event fields corresponds to a keyword model, wherein the keyword model comprises a plurality of keywords and a probability, based on each of the keywords, indicating the text information belongs to one of the M event fields of the keyword model, and wherein the instructions further cause the terminal to:
recognize at least one keyword from the text information;
obtain a probability from the at least one keyword based on the keyword model of each of the M event fields; and
calculate the field probability that the text information belongs to each of the M event fields based on the probability from the at least one keyword.