Method and apparatus for speech synthesis without prosody modification

• US 7,127,396 B2
• Filed: 01/06/2005
• Issued: 10/24/2006
• 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:
  US11/030,208
• Publication Number:
  US 20050119891A1
• Time in Patent Office: 656 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 speech segments for concatenative speech synthesis, the method comprising:

parsing an input text into speech units;

identifying context information for each speech unit based on its location in the input text and at least one neighboring speech unit;

identifying a set of candidate speech segments for each speech unit based on the context information through steps comprising applying the context information for a speech unit to a decision tree to identify a leaf node containing candidate speech segments for the speech unit; and

identifying a sequence of speech segments from the candidate speech segments based in part on a smoothness cost between the speech segments.

2. The method of claim 1 wherein identifying a set of candidate speech segments further comprises pruning some speech segments from a leaf node based on differences between the context information of the speech unit from the input text and context information associated with the speech segments.

3. The method of claim 1 wherein identifying a sequence of speech segments comprises using a smoothness cost that is based on whether two neighboring candidate speech segments appeared next to each other in a training corpus.

4. The method of claim 1 wherein identifying a sequence of speech segments further comprises identifying the sequence based in part on differences between context information for the speech unit of the input text and context information associated with a candidate speech segment.