Unit selection voice synthetic method based on acoustics statistical model

Abstract

The invention relates to a unit selection speech synthesis method based on an acoustic statistical model. The method consists of an extraction of each acoustic feature in training corpus, a training of the statistical model that is corresponding to all kinds of acoustic features combining with the symbol information as syllabic segment and rhythm of each sentence in training corpus and the requirement of statistical model of each acoustic feature corresponding to each phoneme in awaiting synthesis sentences through the text analysis of input text when synthesizes. Based on the norm of maximumlikelihood value between acoustic parameters in awaiting selection unit sequence and acoustic statistical model of awaiting synthesis sentences, the best awaiting selection unit is searched. A KLD between acoustic statistical models is used for achieving fast beforehand selection of synthetic units. Finally, the synthetic speech of a sentence is acquired through smoothing and splicing of best awaiting selection unit waveform of each phoneme.The invention improves the quality of synthetic speech and synthetic effect of traditional splicing and synthetic method and achieves an automatic establishment of the system and an independence of the language kind.

Description

technical field [0001] The invention relates to a unit selection method in waveform splicing speech synthesis, in particular, it guides the selection method of speech segment units by designing and training a group of acoustic statistical models. Background technique [0002] Speech synthesis is an important technology to realize natural and efficient human-computer interaction. There are two most common speech synthesis methods today, one is a synthesis method based on unit selection and waveform splicing, and the other is a parametric synthesis method based on an acoustic statistical model. [0003] In the traditional unit selection algorithm, the target cost and connection cost are often realized by calculating the difference of the context attributes between the units or the distance between the acoustic parameters of the candidate unit and the predicted target. The result of this is that the design of the cost function often requires the participation of language-relat...

AI Technical Summary

Problems solved by technology

However, due to the limitation of the parametric synthesizer, the sound quality of this synthesis method is often not ideal

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