Phoneme-level low-power consumption spoken language assessment and defect diagnosis method

Abstract

The invention discloses a phoneme-level low-power consumption spoken language assessment and defect diagnosis method. The method comprises the steps of (1) conducting acoustic feature extraction on user pronunciation and standard pronunciation to obtain feature vector sequences; (2) decoding the feature vector sequence of the user pronunciation and the feature vector sequence of the standard pronunciation with the Viterbi algorithm based on a weighted finite state transducer Q to obtain the mapping relations between the feature vector sequences and a phoneme sequence, the alignment alpha of the user pronunciation and the alignment (as described in the specification) of the standard pronunciation; (3) comparing the alignment alpha and the feature vector sequence of the user pronunciation with the alignment (as described in the specification) and the feature vector sequence of the standard pronunciation so that user pronunciation quality can be assessed, wherein the higher the goodness of fit is, the better the pronunciation quality is. The method has the advantages that complicated operation is executed in a server, and therefore the load and power consumption of a terminal are effectively reduced; the terminal can be used without networking, and therefore network flow consumption is avoided; phonemes with pronunciation flaws can be recognized and targeted practice can be provided.

Description

technical field [0001] The invention relates to the technical field of computer-aided language learning and speech recognition, in particular to a phonetic-level low-power spoken language evaluation and defect diagnosis method. The phoneme-level technology enables scoring and feedback to be refined to the phoneme level: after the user reads a series of texts, the core phonemes that hinder the user's pure pronunciation can be identified, and corresponding training materials and targeted exercises can be provided. It can be adapted to the learning of English, Chinese, Spanish and other languages, as well as the diagnosis and evaluation of patients with language barriers. Background technique [0002] Language learning is about imitation, especially phonetics. Taking English as an example, in order to practice pure oral English, the best way is to follow the pure pronunciation of native English speakers. Many existing tutorials or tutoring materials are based on this. In gene...

AI Technical Summary

Problems solved by technology

[0003] 1. Due to the difference between listening to your own voice and the voice heard by others, students' perception of their own voice is different from that of others, so it is impossible to objectively score the quality of their own pronunciation

[0004] 2. The above defects can be made up for by means of recording, but switching back and forth between recordings will cause unnecessary trouble and reduce learning efficiency. This is the solution adopted by various repeaters

[0005] 3. Even if the above factors are not taken into account, the evaluation of the students themselves (and even the teacher) is still subjective and qualitative, and objective quantitative evaluation cannot be achieved, and students do not know how to improve

Since users cannot accurately identify their own pronunciation defects, they cannot perform targeted exercises

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