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Large-length voice full-automatic segmentation method

A fully automatic, voice technology, applied in the direction of voice analysis, voice recognition, instruments, etc., can solve the problems of reducing the amount of labeling, the accuracy cannot be guaranteed, etc., and achieve the effect of reducing loss and calculation cost

Inactive Publication Date: 2013-10-09
张巍
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Problems solved by technology

Although the amount of labeling can be greatly reduced, it still cannot guarantee high accuracy

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  • Large-length voice full-automatic segmentation method
  • Large-length voice full-automatic segmentation method
  • Large-length voice full-automatic segmentation method

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Embodiment Construction

[0053] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] Proposal of Sentence Segmentation Algorithm Based on HMM of Spectral Parameters and Prosodic Parameters

[0055] Introduction to Fully Automatic Sentence Segmentation Algorithms

[0056] First of all, we regard the unlabeled automatic sentence segmentation algorithm and the sentence segmentation algorithm based on semi-supervised learning with minimal annotation as two subsystems of the entire sentence automatic segmentation system framework, and regard the former definition based on Force-alignment The algorithm's unlabeled sentence segmentation algorithm is used as a labeling system to provide a small amount of fine-label data for training, and it is renamed Sub-Labeling System (ZLSS). Then use the minimally labeled sentence automatic segmentation algorithm defined by the latter as our classification system, and rena...

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Abstract

The invention relates to a large-length voice full-automatic segmentation method which is a zero-labeling sentence automatic segmentation algorithm having higher accuracy. The algorithm enables a Force-alignment non-supervision algorithm and a semi-supervised learning method based on an HMM to be blended, automatic expansion is carried out on a few precise labeling sets provided for the zero-labeling sentence segmentation algorithm by a semi-supervised learning minimization labeling sentence segmentation algorithm through establishment of an iteration mechanism based on a timer shaft, the purpose of the maximization of the precise labeling sets is realized, and then according to obtained correct periods, voice of an original length is cut into smaller paragraphs or sets of sentences. According to the method, a Force-aligned method under the HMM and a Co_training method in semi-supervised learning are blended together, so the facts that in a large-length voice sentence segmentation process, manual intervention is not needed, and segmentation accuracy is high are guaranteed. The large-length voice full-automatic segmentation method can be applied to rapid and automatic construction of a voice corpus.

Description

technical field [0001] The invention belongs to the technical fields of speech synthesis, speech recognition, speech retrieval and labeling, and relates to a long-length speech automatic segmentation method. Background technique [0002] At present, there are two mainstream speech synthesis methods in the world. One is the trainable (Trainable TTS) speech synthesis method based on HMM, such as CLUSTERGEN of Carnegie Mellon University (CMU) in the United States, and HTS developed by Nagoya Institute of Technology in Japan. Speech synthesis engines, they all use a method based on parametric statistical (Parametric Statistical) synthesis; the other is a speech synthesis method based on a large speech corpus (corpus-based TTS (text to speech)), for example, the Chinese Academy of Sciences Acoustics The proposed KX-PSOLA (1993), and the speech synthesis technology adopted by Xunfei on the telecommunications platform, these are based on the technology of unit selection and wavefor...

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Application Information

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IPC IPC(8): G10L15/04
Inventor 张巍王永远张志楠
Owner 张巍
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