Abstract
With the development of virtual scenes, the degree of simulation and functions of virtual reality have been very complete, providing a new platform and perspective for teaching design. Firstly, the hidden Markov chain model is used to perform emotion recognition on English speech signals. English speech emotion recognition and speech semantic recognition are essentially the same. Hidden Markov style has been widely used in English speech semantic recognition. The experiments of feature extraction and pattern recognition of speech samples prove that Hidden Markovian has higher recognition rate and better recognition effect in speech emotion recognition. Secondly, combining the human pronunciation model and the hearing model, by analyzing the impact of the glottis feature on the human ear hearing-model feature, the research application of the English speech recognition emotion interactive simulation system uses the glottis feature to compensate the human ear, hearing feature is proposed by compensated English speech recognition, and emotion interaction simulation system is used in the English speech emotion experiment, which has obtained a high recognition rate and showed excellent performance.
Highlights
Virtual situation and instructional design are the starting points of this thesis. e teaching research has been concentrated on the discussion of teaching content and teaching methods, and there is almost no teaching research on the perspective and level of time and space and artistic conception. e research on the situation is focused on the research of the “situation teaching” or “situation learning” theory and its teaching methods, or the pure situation or concept research is very fragmented, unsystematic, and not combined with educational discussions
Substitute the training feature into the pattern matching algorithm, train one or several models, and input the test feature into the trained model to find the model with the highest degree of matching; the model is considered to be the representative of the test feature category. e best combination of feature extraction and pattern matching will produce the best recognition effect
Speech synthesis systems are called text-to-speech conversion systems. They are to convert text into speech [5, 6], which corresponds to speech recognition technology. e purpose of speech synthesis technology is to let the computer speak human-like language
Summary
The hidden Markov chain model is used to perform emotion recognition on English speech signals. Hidden Markov style has been widely used in English speech semantic recognition. E experiments of feature extraction and pattern recognition of speech samples prove that Hidden Markovian has higher recognition rate and better recognition effect in speech emotion recognition. Combining the human pronunciation model and the hearing model, by analyzing the impact of the glottis feature on the human ear hearing-model feature, the research application of the English speech recognition emotion interactive simulation system uses the glottis feature to compensate the human ear, hearing feature is proposed by compensated English speech recognition, and emotion interaction simulation system is used in the English speech emotion experiment, which has obtained a high recognition rate and showed excellent performance
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