Device and method for generating recognition network by adjusting recognition vocabulary weights based on a number of times they appear in operation contents

• US 10,002,609 B2
• Filed: 11/13/2014
• Issued: 06/19/2018
• Est. Priority Date: 12/24/2013
• Status: Active Grant

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: Chang, Hsin-Chang, Chen, Jiang-Chun, Kuo, Chih-Chung
• Primary Examiner(s): Kazeminezhad, Farzad
• Application Number:
  US14/541,116
• Publication Number:
  US 20150179171A1
• Time in Patent Office: 1,314 Days
• Field of Search: 704 8, 704246
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 15/32 Multiple recognisers used in sequence or in parallel; Score combination systems therefor, e.g. voting systems

Abstract

A recognition network generation device, disposed in an electronic device, comprising: an operation record storage device storing a plurality of operation records of the electronic device, wherein each of the operation records includes operation content executed by the electronic device and device peripheral information detected by the electronic device when the electronic device executes the operation content; an activity model constructor classifying the operation records into a plurality of activity models according to all the device peripheral information of the operation records; an activity predictor selecting at least one selected activity model according to the degree of similarity between each of the activity models and a current device peripheral information detected by the electronic device; and a weight adjustor adjusting the weights of a plurality of recognition vocabularies by taking into account a number of times each recognition vocabulary appears in all operations contents of the activity models, wherein the recognition vocabularies correspond to all the operation content of the at least one selected activity model.

Claims

1. A recognition network generation device, disposed in an electronic device, comprising:

an operation record storage device storing a plurality of operation records of the electronic device, wherein each of the operation records comprises an operation content which is executed by the electronic device and a piece of device peripheral information detected by the electronic device when the electronic device executes the operation content, and wherein each operation content comprises at least a target vocabulary or a function vocabulary, and each operation content further comprises an application title;

an activity model constructor coupled to the operation record storage device classifying the operation records into a plurality of activity models according to all the device peripheral information of the operation records;

an activity predictor selecting at least one selected activity model from all the activity models according to a degree of similarity between each of the activity models and a current device peripheral information detected by the electronic device;

a weight adjustor adjusting weights of a plurality of recognition vocabularies, wherein the recognition vocabularies correspond to the target vocabularies and function vocabularies of all operation contents of the at least one selected activity model; and

a recognizer, receiving a speech input and being coupled to the weight adjustor to receive weight adjustment information output by the weight adjustor;

wherein the weight adjustor adjusts the weight of each of the recognition vocabularies of all the operation contents of the at least one selected activity model according to a first number of times and a second number of times, wherein the first number of times is defined as the number of times that each of the recognition vocabularies appears in all of the operation contents of the at least one selected activity model and the second number of times is defined as the number of times that each of the application titles associated with each of the recognition vocabularies appears in all of the operation contents of the at least one selected activity model;

wherein the recognizer stores a recognition lexicon and uses the weight adjustment information to update the recognition lexicon; and

wherein the recognizer outputs a recognized target vocabulary according to the speech input and the updated recognition lexicon, wherein the recognized target vocabulary is one of the target vocabularies in the updated recognition lexicon.

2. The recognition network generation device of claim 1, wherein the activity model constructor converts each piece of the device peripheral information into a characteristic data respectively;

wherein the activity model constructor classifies the characteristic data into the activity models according to the degree of similarity between all the characteristic data; and

wherein each of the activity models generates a representative characteristic data respectively according to the characteristic data belonging to each of the activity models.

3. The recognition network generation device of claim 2, wherein the activity predictor converts the current device peripheral information into a current characteristic data;

the activity predictor calculates a similarity coefficient corresponding to each of the activity models according to the degree of similarity between each of the representative characteristic data and the current device peripheral information; and

the activity predictor selects the at least one selected activity model according to the similarity coefficient.

4. The recognition network generation device of claim 1, wherein the electronic device further comprises a display;

wherein the recognizer, from all the operation contents, searches each of the function vocabularies belonging to the same operation content that the recognized target vocabulary belonging to, and sorts each of the searched function vocabularies according to the second number of times of the application title corresponding to each of the searched function vocabularies; and

wherein the display is coupled to the recognizer and displays the recognized target vocabulary and the sorted function vocabularies received from the recognizer.

5. The recognition network generation device of claim 4, wherein a user selects a recognized function vocabulary from all the sorted function vocabularies;

and

the recognizer receives the recognized function vocabulary and outputs the recognized target vocabulary and the recognized function vocabulary as a speech recognition result.

6. The recognition network generation device of claim 1, wherein each of the device peripheral information comprises at least one of a sensing device status, a time status, a position status, a moving status, a wireless transmission interface status and a wired transmission interface status.

7. A recognition network generation method, comprising:

storing, by an operation record storage device, a plurality of operation records of an electronic device, wherein each of the operation records comprises an operation content which is executed by the electronic device and a piece of device peripheral information detected by the electronic device when the electronic device executes the corresponding operation content, and wherein each operation content comprises at least a target vocabulary or a function vocabulary, and each operation content further comprises an application title;

classifying, by an activity model constructor, the operation records into a plurality of activity models according to all the device peripheral information of the operation records;

selecting, by an activity predictor, at least one selected activity model from all activity models according to degree of similarity between each of the activity models and a current device peripheral information detected by the electronic device;

and adjusting, by a weight adjustor, weights of a plurality of recognition vocabularies, wherein the recognition vocabularies correspond to the target vocabularies and function vocabularies of all operation contents of the at least one selected activity model;

wherein the weight adjustor adjusts the weight of each of the recognition vocabularies of all the operation contents of the at least one selected activity model according to a first number of times and a second number of times, wherein the first number of times is defined as the number of times that each of the recognition vocabularies appears in all of the operation contents of the at least one selected activity model and the second number of times is defined as the number of times that each of the application titles associated with each of the recognition vocabularies appears in all of the operation contents of all the at least one selected activity model;

wherein a recognizer receives a speech input and is coupled to the weight adjustor to receive a weight adjustment information output by the weight adjustor;

wherein the recognizer further stores a recognition lexicon and uses the weight adjustment information to update the recognition lexicon; and

wherein the recognizer further outputs a recognized target vocabulary according to the speech input and the updated recognition lexicon, wherein the recognized target vocabulary is one of the target vocabularies in the updated recognition lexicon.

8. The recognition network generation method of claim 7, further comprising:

converting, by the activity model constructor, each piece of the device peripheral information into a characteristic data respectively; and

classifying, by the activity model constructor, the characteristic data into the activity models according to the degree of similarity between all the characteristic data, wherein each of the activity models generates a representative characteristic data respectively.

9. The recognition network generation method of claim 8, further comprising:

converting, by the activity predictor, the current device peripheral information into a current characteristic data;

calculating, by the activity predictor, a similarity coefficient corresponding to each of the activity models according to the degree of similarity between each of the representative characteristic data and the current device peripheral information; and

selecting, by the activity predictor, at least one selected activity model according to the similarity coefficient.

10. The recognition network generation method of claim 7, further comprising:

searching, by the recognizer, each of the function vocabularies belonging to the same operation content that the recognized target vocabulary belonging to from all the operation contents; and

sorting, by the recognizer, all the searched function vocabularies according to the second number of times of the application title corresponding to each of the searched function vocabularies.

11. The recognition network generation method of claim 10, wherein a user selects a recognized function vocabulary from all the sorted function vocabularies;

and

outputting, by the recognizer, the recognized target vocabulary and the recognized function vocabulary as a speech recognition result.

12. The recognition network generation method of claim 7, wherein each of the device peripheral information comprises at least one of a sensing device status, a time status, a position status, a moving status, a wireless transmission interface status and a wired transmission interface status.

13. A recognition network generation device disposed in an electronic device comprising:

a storage unit stores a plurality of operation records of the electronic device, wherein each of the operation records comprises an operation content which is executed by the electronic device and a piece of device peripheral information detected by the electronic device when the electronic device executes the operation content, and wherein each operation content comprises at least a target vocabulary or a function vocabulary, and each operation content further comprises an application title;

a recognizer; and

a processor coupled to the storage unit loads and performs a recognition network generation program which includes the steps of;

classifying, by the processor, the operation records into a plurality of activity models according to all the device peripheral information of the operation records;

selecting, by the processor, at least one selected activity model from all the activity models according to degree of similarity between each of the activity models and a current device peripheral information detected by the electronic device;

adjusting, by the processor, weights of a plurality of recognition vocabularies, wherein the recognition vocabularies correspond to the target vocabularies and function vocabularies of all operation contents of the at least one selected activity model; and

recognizing, by the recognizer, a speech input by a weight adjustment information and outputs a speech recognition result;

wherein the processor adjusts the weight of each of the recognition vocabularies of all the operation contents of the at least one selected activity model according to a first number of times and a second number of times, wherein the first number of times is defined as the number of times that each of the recognition vocabularies appears in all of the operation contents of the at least one selected activity model and the second number of times is defined as the number of times that each of the application titles associated with each of the recognition vocabularies appears in all of the operation contents of the at least one selected activity model.

14. The recognition network generation device of claim 13, wherein classifying, by the processor, the operation records into a plurality of activity models according to all the device peripheral information of the operation records further comprising:

converting each piece of the device peripheral information into a characteristic data respectively;

classifying the characteristic data into the activity models according to the degree of similarity between all the characteristic data; and

generating, by each of the activity models, a representative characteristic data respectively according to the characteristic data belonging to each of the activity models.

15. The recognition network generation device of claim 13, wherein each of the device peripheral information comprises at least one of a sensing device status, a time status, a position status, a moving status, a wireless transmission interface status and a wired transmission interface status.