Generating audio rendering from textual content based on character models

• US 10,607,595 B2
• Filed: 08/07/2017
• Issued: 03/31/2020
• Est. Priority Date: 08/07/2017
• Status: Active Grant

• Current Assignee: Lenovo PC International Limited (Lenovo Group Ltd.)
• Original Assignee: Lenovo Singapore Pte Limited (Lenovo Group Ltd.)
• Inventors: Kingsbury, Timothy Robert, Kapinos, Robert James, Li, Scott Wentao, VanBlon, Russell Speight
• Primary Examiner(s): Azad, Abul K
• Application Number:
  US15/670,117
• Publication Number:
  US 20190043474A1
• Time in Patent Office: 967 Days
• Field of Search: None
• CPC Class Codes: G06F 40/117 Tagging; Marking up details of markup languages G06F40/143; Designating a block; Setting of attributes style sheets, e.g. eXtensible Stylesheet Language Transformation [XSLT], G06F40/154
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/006 based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G10L 13/0335 Pitch control
• CPC Class Codes: G10L 13/047 Architecture of speech synthesisers
• CPC Class Codes: G10L 13/10 Prosody rules derived from text; Stress or intonation

Abstract

A computer implemented method, device and computer program product are provided. The method, device and computer program product utilize textual machine learning to analyze textual content to identify narratives for associated content segments of the textual content. The method, device and computer program further utilize textual machine learning to designate character models for the corresponding narratives and generate an audio rendering of the textual content utilizing the character models in connection with the corresponding narratives for the associated content segments.

Claims

1. A computer implemented method, comprising:

under control of one or more processors configured with specific executable program instructions,

analyzing textual content to identify narratives for associated content segments of the textual content;

utilizing textual machine learning to designate character models for the corresponding narratives based on one or more of a trait of the narrative and/or a nature of the textual content, wherein the designating utilizes the textual machine learning to designate different intonations to be utilized by at least one of the character models in connection with sections of the corresponding narrative based on traits of the narrative; and

generating an audio rendering of the textual content utilizing the character models in connection with the corresponding narratives for the associated content segments.

2. The method of claim 1, wherein the designating utilizes the textual machine learning to designate the character models to simulate different characters within a book based on traits of the narrative, the characters differing from one another by one or more of age, sex, race, nationality or personality traits.

3. The method of claim 1, wherein the content segments represent sections of spoken dialogue by different individual speakers, and wherein the textual machine learning assigns different character models for the individual speakers based on traits of the narrative.

4. The method of claim 1, further comprising identifying a dialogue tag associated with the content segment and assigning at least one of the intonations based on dialogue tag.

5. The method of claim 1, wherein the textual content represents a story and the audio rendering includes a first character model representing a narrator and secondary character models representing characters within the story, the textual machine learning designating the first and second character models based on traits of the narrative.

6. The method of claim 1, wherein the textual content includes dialogue tags, the method further comprising removing the dialogue tags from the audio rendering.

7. The method of claim 1, further comprising receiving feedback regarding one or more character models in the audio rendering and adjusting the one or more character models based on the feedback.

8. A device, comprising:

a processor;

a memory storing program instructions accessible by the processor;

wherein, responsive to execution of the instructions, the processor performs the following;

analyzing textual content to identify narratives for associated content segments of the textual content;

utilizing textual machine learning to designate character models for the corresponding narratives based on traits of the narrative, wherein the designating utilizes the textual machine learning to designate different intonations to be utilized by at least one of the character models in connection with sections of the corresponding narrative based on traits of the narrative; and

generating an audio rendering of the textual content utilizing the character models in connection with the corresponding narratives for the associated content segments.

9. The device of claim 8, wherein the processor designates the character models to simulate different characters within a book, the characters differing from one another by one or more of age, sex, race, nationality or personality traits, the textual machine learning designating the character models based on the traits of the narrative.

10. The device of claim 8, wherein the processor steps through content segments and assigns the content segment to a new narrative or a pre-existing narrative.

11. The device of claim 8, wherein the processor compares current and pre-existing narrative traits to determine whether to update or replace the corresponding character model.

12. The device of claim 8, wherein the textual content represents a story and the audio rendering includes a first character model representing a narrator and secondary character models represent characters within the story, the textual machine learning designating the first and second character models based on traits of the narrative.

13. The device of claim 8, wherein the textual content includes dialogue tags, the processor to remove the dialogue tags from the audio rendering.

14. The device of claim 8, wherein the processor to receive feedback regarding one or more character models in the audio rendering and adjust the one or more character models based on the feedback.

15. A computer program product comprising a non-signal computer readable storage medium comprising computer executable code, which when executed by a processor performs:

analyzing textual content to identify narratives for associated content segments of the textual content;

utilizing textual machine learning to designate character models for the corresponding narratives based on one or more of a trait of the narrative and/or a nature of the textual content, wherein the designating utilizes the textual machine learning to designate different intonations to be utilized by at least one of the character models in connection with sections of the corresponding narrative based on traits of the narrative; and

generating an audio rendering of textual content utilizing character models in connection with corresponding narratives.

16. The computer program product of claim 15, wherein the computer executable code comprises the character models to simulate different characters within a book based on the traits of the narrative, the characters differing from one another by one or more of age, sex, race, nationality or personality traits.

17. The computer program product of claim 15, wherein the content segments represent sections of spoken dialogue by different individual speakers, and wherein the computer executable code to assign different character models for the individual speakers based on the traits of the narrative.