Training and applying prosody models

• US 8,554,566 B2
• Filed: 11/29/2012
• Issued: 10/08/2013
• Est. Priority Date: 08/12/2008
• Status: Active Grant

• Current Assignee: Morphism LLC
• Original Assignee: Morphism LLC
• Inventors: Stephens, Jr., James H.
• Primary Examiner(s): GUERRA-ERAZO, EDGAR X
• Application Number:
  US13/689,527
• Publication Number:
  US 20130085760A1
• Time in Patent Office: 313 Days
• Field of Search: 704/260, 704/261, 704/258, 704/254, 704/267, 704/266, 704/268, 704/269, 704/235, 704/247, 704/270, 704/275, 704/276, 704/249, 704/231, 704/246, 704/234
• US Class Current: 704/260
• CPC Class Codes: G10L 13/08 Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
• CPC Class Codes: G10L 13/10 Prosody rules derived from text; Stress or intonation
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/1807 using prosody or stress

Abstract

Techniques for training and applying prosody models for speech synthesis are provided. A speech recognition engine processes audible speech to produce text annotated with prosody information. A prosody model is trained with this annotated text. After initial training, the model is applied during speech synthesis to generate speech with non-standard prosody from input text. Multiple prosody models can be used to represent different prosody styles.

Claims

1. A computer-implementable method for synthesizing audible speech, with varying prosody, from textual content, the method comprising:

maintaining an inventory of prosody models with lexicons,

selecting a subset of multiple prosody models from the inventory of prosody models;

associating prosody models in the subset of multiple prosody models with different segments of a text based on phrases in the text statistically associated with the lexicons of the prosody models;

applying the associated prosody models to the different segments of the text to produce prosody annotations of the text;

considering annotations of the prosody annotations to reconcile conflicting prosody annotations of the text previously produced by multiple prosody models associated with a segment of the text; and

synthesizing audible speech from the text and the reconciled prosody annotations.

2. The method of claim 1, wherein the reconciling is based on a reconciliation policy.

3. The method of claim 2, wherein the reconciliation policy considers the annotations of the prosody annotations that comprise a prosody model identifier and a prosody model confidence for the prosody annotation.

4. The method of claim 2, wherein the reconciliation eliminates conflicting annotations that result from applications of multiple models.

5. The method of claim 3, wherein the selecting is based on input parameters that indicate geographical information about a speaker of the text.

6. The method of claim 3, wherein the selecting is based on input parameters that indicate the type, identity, or role of a speaker of the text.