Abstract: A method for training a speech-emotion recognition classifier under a continuously updatable and re-trainable ASER machine learning model. The quantified training data is generated by first processing the utterances of a human speech source and the associated texts in an emotion evaluation and rating process with normalization; then, extracting the features of the utterance; quantifying the feature attributes of the extracted features by labelling, tagging, and weighting the feature attributes, with their values assigned under measurable scales. The quantified training data comprises the normalized results of the emotion evaluation and rating process, the extracted features, the quantified emotional feature attributes, and the hash values of the quantified emotional feature attributes.

Abstract: A method for training a speech-emotion recognition classifier under a continuous self-updating and re-trainable ASER machine learning model, wherein the training data is generated by: obtaining an utterance of a human speech source; processing the utterance in an emotion evaluation and rating process with normalization; extracting the features of the utterance; quantifying the feature attributes of the extracted features by labelling, tagging, and weighting the feature attributes, with their values assigned under measurable scales; and hashing the quantified feature attributes in a feature attribute hashing process to obtain hash values for creating a feature vector space.