Phonetic-level low power consumption spoken language evaluation and defect diagnosis method without standard pronunciation

Abstract

The invention discloses a phonetic-level low power consumption spoken language evaluation and defect diagnosis method without standard pronunciation. The method includes the following steps that (1) acoustic feature extraction is conducted on user pronunciation to obtain a feature vector sequence; (2) decoding operation is conducted on the feature vector sequence of user pronunciation through a Viterbi algorithm based on a weighting finite state converter Q, so that the mapping relation from the feature vector sequence to a phoneme sequence is obtained; (3) with respect to each phoneme, the pronunciation quality of the user on each phoneme is evaluated by calculating the goodness of fit between a feature vector set corresponding to the phonemes and the mathematic representation of the phonemes in an acoustic model H. The phonetic-level low power consumption spoken language evaluation and defect diagnosis method without standard pronunciation has the advantages that the method is independent of standard pronunciation, all burdensome calculations are executed on a server terminal, the calculation amount of a user terminal is minimized, the load and power consumption on the terminals are effectively reduced, in the using process of the terminals, networking is needless, and network flow consumption is avoided; the phonemes which the user cannot pronounce well can be recognized, and targeted training is 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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