Corpus-based prosody translation system

• US 20020152073A1
• Filed: 10/01/2001
• Published: 10/17/2002
• Est. Priority Date: 09/29/2000
• Status: Active Grant

• Current Assignee: Cerence Operating Company (Cerence Inc.)
• Original Assignee: Lernout & Hauspie Speech Products NV (Intel Corporation)
• Inventors: Fackrell, Justin, Van Coile, Bert, Rutten, Peter, DeMoortel, Jan
• Granted Patent: US 7,069,216 B2
• Time in Patent Office: Days
• US Class Current: 704/260
• CPC Class Codes: G10L 13/10 Prosody rules derived from text; Stress or intonation

Abstract

A method of prosody translation is given. A target input symbol sequence is provided, including a first set of speech prosody descriptors. An instance-based learning algorithm is applied to a corpus of speech unit descriptors to select an output symbol sequence representative of the target input symbol sequence and including a second set of speech prosody descriptors. The second set differs from the first set.

Claims

1. A method of translating speech prosody comprising:

providing a target input symbol sequence including a first set of speech prosody descriptors; and

applying an instance-based learning algorithm to a corpus of speech unit descriptors to select an output symbol sequence representative of the target input symbol sequence and including a second set of speech prosody descriptors, the second set differing from the first set.

2. A method according to claim 1, wherein the speech unit descriptors are associated with short speech units (SSUs).

3. A method according to claim 2, wherein the SSUs are diphones.

4. A method according to claim 2, wherein the SSUs are demi-phones.

5. A method according to claim 1, wherein the target input symbol sequence is produced by processing an input text sequence to extract prosodic features.

6. A method according to claim 1, further comprising concatenating the output symbol sequence to produce an output prosody track corresponding to the target input symbol sequence for use by a speech processing application.

7. A method according to claim 6, wherein the speech processing application includes a text-to-speech application.

8. A method according to claim 6, wherein the speech processing application includes a prosody labeling application.

9. A method according to claim 6, wherein the speech processing application includes an automatic speech recognition application.

10. A method according to claim 1, wherein the algorithm determines accumulated matching costs associated with candidate sequences of speech unit descriptors in the corpus representative of the how well each candidate sequence matches the target input symbol sequence, such that the output symbol sequence represents the candidate sequence having the smallest accumulated matching costs.

11. A method according to claim 10, wherein the matching costs include a node cost representative of the how well symbolic descriptors in the candidate sequence match symbolic descriptors in the target input symbols sequence.

12. A method according to claim 10, wherein the matching costs include a transition cost representative of how well acoustic descriptors in the candidate sequence match acoustic descriptors in the target input symbol sequence.