Method and apparatus for speech synthesis without prosody modification

• US 6,978,239 B2
• Filed: 05/07/2001
• Issued: 12/20/2005
• Est. Priority Date: 12/04/2000
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Peng, Hu, Chu, Min
• Primary Examiner(s): Abebe, Daniel
• Application Number:
  US09/850,527
• Publication Number:
  US 20020099547A1
• Time in Patent Office: 1,688 Days
• Field of Search: 704/258, 704/260
• US Class Current: 704/258
• CPC Class Codes: G10L 13/07 Concatenation rules

Abstract

A speech synthesizer is provided that concatenates stored samples of speech units without modifying the prosody of the samples. The present invention is able to achieve a high level of naturalness in synthesized speech with a carefully designed training speech corpus by storing samples based on the prosodic and phonetic context in which they occur. In particular, some embodiments of the present invention limit the training text to those sentences that will produce the most frequent sets of prosodic contexts for each speech unit. Further embodiments of the present invention also provide a multi-tier selection mechanism for selecting a set of samples that will produce the most natural sounding speech.

Claims

1. A method of selecting sentences for reading into a training speech corpus used in speech synthesis, the method comprising:

identifying a set of prosodic context information for each of a set of speech units;

determining a frequency of occurrence for each distinct context vector that appears in a very large text corpus;

using the frequency of occurrence of the context vectors to identify a list of necessary context vectors; and

selecting sentences in the large text corpus for reading into the training speech corpus, each selected sentence containing at least one necessary context vector.

2. The method of claim 1 wherein identifying a collection of prosodic context information sets as necessary context information sets comprises:

determining the frequency of occurrence of each prosodic context information set across a very large text corpus; and

identifying a collection of prosodic context information sets as necessary context information sets based on their frequency of occurrence.

3. The method of claim 2 wherein identifying a collection of prosodic context information sets as necessary context information sets further comprises:

sorting the context information sets by their frequency of occurrence in decreasing order;

determining a threshold, F, for accumulative frequency of top context vectors; and

selecting the top context vectors whose accumulative frequency is not smaller than F for each speech unit as necessary prosodic context information sets.

4. The method of claim 1 further comprising indexing only those speech segments that are associated with sentences in the smaller training text and wherein indexing comprises indexing using a decision tree.

5. The method of claim 4 wherein indexing further comprises indexing the speech segments in the decision tree based on information in the context information sets.

6. The method of claim 5 wherein the decision tree comprises leaf nodes and at least one leaf node comprises at least two speech segments for the same speech unit.