Method and system for graded measurement of voice

Abstract

The invention relates to a method and a system for graded measurement of voice. The method comprises the following steps: carrying out voice recognition for a received voice signal, and acquiring a voice feature sequence of state alignment according to a reference text and a reference model; correcting a distribution parameter of the reference model according to the voice feature sequence of state alignment, and generating a voice template vector based on the reference model for the voice signal; using a support vector machine classification decision tree to carry out classification decision for the voice template vector, and then, obtaining the classification grade mapped by the voice template vector. A support vector machine is provided by the invention to build a model for a language classification boundary, and the model is applied to language learning with the following steps: fetching the voice feature of the received voice signal, and carrying out state alignment between the received voice signal and the reference model; correcting the distribution parameter of the reference model and generating a corresponding voice template vector; and using the support vector machine classification decision tree to decide the voice template vector, thus, the complexity of the classification decision of voice is effectively reduced, and the accuracy of the graded measurement of voice is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for efficient training of models that can accurately classifies audio signals based on their linguistic characteristics or other factors like speaking style. It uses Support Vector Machines (SVM) techniques to create templates representing specific aspects of spoken words during conversations between users' devices. These templates are then compared against existing ones to identify patterns called phone numbers. Based upon these identified patterns, an algorithm predictively assigns them to different languages depending on how well they match each others. By comparing this prediction with pre-existing data sets about English speakers, it provides valuable insights towards understanding speaker styles better than just typing out individual characters. Overall, this technique helps improve communication efficiency while reducing complexities associated therewith such as recognizing pronunciation differences and identifying certain sounds.

Problems solved by technology

This patented technical problem addressed by this patents relates to improving speech recognization models trained with classical methods like HML or hidden markov models while also being able to accurately identify specific types of sound patterns called nonsymphony sounds. These existing techniques have limitations including difficulty identifying subtle differences among speaker accords and requiring significant resources during training process.

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