IP Library › Granted Patent US 10,923,102
Granted Patent B2
US 10,923,102 · App. 15/991,411 · Granted Feb 16, 2021

Method and apparatus for broadcasting a response based on artificial intelligence, and storage medium

Inventors: Yu Wang (Beijing, CN); Bo Xie (Beijing, CN)
Assignee: Baidu Online Network Technology (Beijing) Co., LTD.
G10L13/027G10L15/1815G10L15/22G10L25/63G10L2015/223
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Quick Facts
Patent No.
US 10,923,102
App. No.
15/991,411
Granted
Feb 16, 2021
Kind
B2
Abstract

The present disclosure provides a method and apparatus for broadcasting a response based on artificial intelligence, and a storage medium, wherein the method comprises: obtaining a user-input speech query; generating a response corresponding to the query; obtaining a recorded speech of a mood meaning corresponding to a modal particle in the response and matched with the response; combining the obtained recorded speech with a TTS-generated speech to perform TTS broadcast of the response. The solution of the present disclosure may be applied to enhance an effect of broadcasting the response.

Claims (63)

1. A method for broadcasting a response based on artificial intelligence, wherein the method comprises:

obtaining a user-input speech query;

generating a response in a text form corresponding to the query;

in response to determining that the response includes a modal particle, determining a mood meaning of the modal particle expressed in the response, and and obtaining a pre-recorded speech of the modal particle having the mood meaning; and

combining the obtained pre-recorded speech with a Text-To-Speech-generated speech to perform Text-To-Speech broadcast of the response.

2. The method according to claim 1 , wherein

the generating a response corresponding to the query comprises:

determining a demand corresponding to the query;

selecting one response-generating algorithm from M response-generating algorithms corresponding to the demand, M being a positive integer larger than 1; and

using the selected response-generating algorithm to generate the response.

3. The method according to claim 2 , wherein

the determining a demand corresponding to the query comprises:

performing speech recognition for the query to obtain a speech recognition result; and

determining a demand corresponding to the query by performing semantic parsing for the speech recognition result;

the selecting one response-generating algorithm from M response-generating algorithms corresponding to the demand comprises:

randomly selecting one response-generating algorithm from the M response-generating algorithms corresponding to the demand.

4. The method according to claim 1 , wherein

at least one pre-recorded speech is pre-generated for the modal particle, and each pre-recorded speech corresponds to a different mood meaning.

5. The method according to claim 1 , wherein

the combining the obtained pre-recorded speech with a Text-To-Speech-generated speech to perform Text-To-Speech broadcast of the response comprises:

when it is needed to broadcast the modal particle corresponding to the obtained pre-recorded speech, broadcasting the obtained pre-recorded speech, otherwise broadcasting the Text-To- Speech-generated speech.

6. A computer device, comprising a memory, a processor and a computer program which is stored on the memory and runs on the processor, wherein the processor, upon executing the program, implements the following operation:

obtaining a user-input speech query;

generating a response in a text form corresponding to the query;

in response to determining that the response includes a modal particle, determining a mood meaning of the modal particle expressed in the response, and obtaining a pre-recorded speech of the mood meaning having the mood meaning; and

combining the obtained pre-recorded speech with a Text-To-Speech-generated speech to perform Text-To-Speech broadcast of the response.

7. The computer device according to claim 6 , wherein

the generating a response corresponding to the query comprises:

determining a demand corresponding to the query;

selecting one response-generating algorithm from M response-generating algorithms corresponding to the demand, M being a positive integer larger than 1; and

using the selected response-generating algorithm to generate the response.

8. The computer device according to claim 7 , wherein

the determining a demand corresponding to the query comprises:

performing speech recognition for the query to obtain a speech recognition result; and

determining a demand corresponding to the query by performing semantic parsing for the speech recognition result;

the selecting one response-generating algorithm from M response-generating algorithms corresponding to the demand comprises:

randomly selecting one response-generating algorithm from the M response-generating algorithms corresponding to the demand.

9. The computer device according to claim 6 , wherein

pre-recorded speech is pre-generated for the modal particle, and each pre-recorded speech corresponds to a different mood meaning.

10. The computer device according to claim 6 , wherein

the combining the obtained pre-recorded speech with a Text-To-Speech-generated speech to perform Text-To-Speech broadcast of the response comprises:

when it is needed to broadcast the modal particle corresponding to the obtained pre- recorded speech, broadcasting the obtained pre-recorded speech, otherwise broadcasting the Text- To-Speech-generated speech.

11. A non-transitory computer-readable storage medium on which a computer program is stored, wherein the program, when executed by a processor, implements the following operation:

obtaining a user-input speech query;

generating a response in a text form corresponding to the query;

in response to determining that the response includes a modal particle, determining a mood meaning of the modal particle expressed in the response, and obtaining a pre-recorded speech of the modal particle having the mood meaning; and

combining the obtained pre-recorded speech with a Text-To-Speech-generated speech to perform Text-To-Speech broadcast of the response.

12. The non-transitory computer-readable storage medium according to claim 11 , wherein

the generating a response corresponding to the query comprises:

determining a demand corresponding to the query;

selecting one response-generating algorithm from M response-generating algorithms corresponding to the demand, M being a positive integer larger than 1; and

using the selected response-generating algorithm to generate the response.

13. The non-transitory computer-readable storage medium according to claim 12 , wherein

the determining a demand corresponding to the query comprises:

performing speech recognition for the query to obtain a speech recognition result; and

determining a demand corresponding to the query by performing semantic parsing for the speech recognition result;

the selecting one response-generating algorithm from M response-generating algorithms corresponding to the demand comprises:

randomly selecting one response-generating algorithm from the M response-generating algorithms corresponding to the demand.

14. The non-transitory computer-readable storage medium according to claim 11 , wherein

at least one pre-recorded speech is pre-generated for the modal particle, and each pre-recorded speech corresponds to a different mood meaning.

15. The non-transitory computer-readable storage medium according to claim 11 , wherein

the combining the obtained pre-recorded speech with a Text-To-Speech-generated speech to perform Text-To-Speech broadcast of the response comprises:

when it is needed to broadcast the modal particle corresponding to the obtained pre-recorded speech, broadcasting the obtained pre-recorded speech, otherwise broadcasting the Text-To-Speech-generated speech.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 4, 2018
From: WANG, YU; XIE, BO
To: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) CO., LTD.
Reel/Frame 045979/0119 →
Priority Claims (1)
CN 2017 1 04806773 · Jun 22, 2017 · national
Continuity (1)
Related Publication 20180374474A1 · Dec 27, 2018
Cited By (1)
US 12,499,868