Automatic Segmentation in Speech Synthesis

• US 20090313025A1
• Filed: 08/20/2009
• Published: 12/17/2009
• Est. Priority Date: 03/29/2002
• Status: Active Grant

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: AT&T Corporation (AT&T, Inc.)
• Inventors: CONKIE, Alistair D., KIM, Yeon-Jun
• Granted Patent: US 8,131,547 B2
• Time in Patent Office: Days
• US Class Current: 704/266
• CPC Class Codes: G10L 13/06 Elementary speech units used in speech synthesisers; Concatenation rules

Abstract

A method and system are disclosed that automatically segment speech to generate a speech inventory. The method includes initializing a Hidden Markov Model (HMM) using seed input data, performing a segmentation of the HMM into speech units to generate phone labels, correcting the segmentation of the speech units. Correcting the segmentation of the speech units includes re-estimating the HMM based on a current version of the phone labels, embedded re-estimating of the HMM, and updating the current version of the phone labels using spectral boundary correction. The system includes modules configured to control a processor to perform steps of the method.

Claims

1. A method for automatic segmentation of speech to generate a speech inventory, the method comprising:

initializing, via a processor, a Hidden Markov Model (HMM) using seed input data;

performing a segmentation of the HMM into speech units to generate phone labels;

correcting, via the processor, the segmentation of the speech units by performing the steps;

re-estimating the HMM based on a current version of the phone labels;

embedded re-estimating of the HMM; and

updating the current version of the phone labels using spectral boundary correction.

2. The method of claim 1, further comprising concatenating the speech units to synthesize speech.

3. The method of claim 2, further comprising iteratively performing the re-estimating, embedded re-estimating, and updating steps until no perceptual improvement of synthesis quality is detected between iterations.

4. The method of claim 1, wherein the seed input data is selected from the group consisting of hand-labeled bootstrapped data, speaker-independent HMM bootstrapped data, and flat start data.

5. The method of claim 1, further comprising adjusting boundaries of the phone labels within specified time windows.

6. The method of claim 1, further comprising identifying context-dependent time windows around speech unit boundaries, wherein the speech unit boundaries include one or more of:

a vowel-to-vowel boundary;

a vowel-to-nasal boundary;

a vowel-to-voiced stop boundary;

a vowel-to-liquid boundary;

a vowel-to-unvoiced stop boundary;

a vowel-to-voiced fricative boundary;

an unvoiced stop-to-vowel boundary;

a nasal-to-vowel boundary;

a voiced stop-to-vowel boundary

a liquid-to-vowel boundary;

an unvoiced fricative-to-vowel boundary; and

a voiced fricative-to-vowel boundary.

7. The method of claim 6, wherein the context-dependent time windows are empirically determined by adjacent phones.

8. A computer-readable storage medium storing a set of program instructions executable on a processor device and usable to reduce speech unit boundaries, the instructions causing the processing device to perform the steps:

aligning a trained set of HMMs to produce phone labels that are segmented, wherein each phone label has a spectral boundary;

performing a spectral boundary correction on the phone labels, wherein spectral boundary correction re-aligns each spectral boundary using bending points of spectral transitions; and

synthesizing speech using the phone labels having spectral boundary correction.

9. The computer-readable storage medium of claim 8, wherein the instructions further comprise bootstrapping the set of HMMs with at least one of speaker-dependent HMMs and speaker-independent HMMs.

10. The computer-readable storage medium of claim 8, wherein the instructions further comprise:

initializing the set of HMMs;

re-estimating the set of HMMs; and

performing embedded re-estimation on the set of HMMs.

11. The computer-readable storage medium of claim 8, wherein the instruction further comprise performing a Viterbi alignment on the trained set of HMMs to produce phone labels that are segmented.

12. The computer-readable storage medium of claim 10, wherein the instructions further comprise iteratively performing a first alignment on a trained set of HMMs to produce phone labels that are segmented and performing spectral boundary correction on the phone labels.

13. The computer-readable storage medium of claim 12, wherein the instructions further comprise training the set of HMMs using phone labels having boundaries that have been re-aligned using spectral boundary correction.

14. The computer-readable storage medium of claim 8, wherein the instructions further comprise performing spectral boundary correction on the phone labels within a context-dependent time window.

15. The computer-readable storage medium of claim 14, wherein the instructions further comprise determining empirically the context-dependent time window using adjacent phones.

16. The computer-readable storage medium of claim 8, wherein each spectral boundary is between a first phone class and a second phone class.

17. A system for automatic segmentation of speech to generate a speech inventory, the system comprising:

a processor;

a module configured to control the processor to initialize a Hidden Markov Model (HMM) using seed input data;

a module configured to control the processor to perform a segmentation of the HMM into speech units to generate phone labels;

a module configured to control the processor to correct the segmentation of the speech units by performing the steps;

re-estimating the HMM based on a current version of the phone labels;

embedded re-estimating of the HMM; and

updating the current version of the phone labels using spectral boundary correction.

18. The system of claim 17, further comprising a module configured to control the processor to concatenate the speech units to synthesize speech.

19. The system of claim 18, further comprising a module configured to control the processor to iteratively perform the re-estimating, embedded re-estimating, and updating steps until no perceptual improvement of synthesis quality is detected between iterations.

20. The system of claim 17, wherein the seed input data is selected from the group consisting of hand-labeled bootstrapped data, speaker-independent HMM bootstrapped data, and flat start data.