A method of training a speech-emotion recognition classifier under a continuously self-updating and retrainable ASER machine learning model, wherein training data is generated by: obtaining an utterance of a human speech source; performing normalization processing on the utterances in the emotion evaluation and rating process; extracting utterance features; the feature attributes of the extracted features are quantified by marking, labeling and weighting the feature attributes, and values of the feature attributes are distributed under a measurable scale; the quantized feature attributes are hashed in the feature attribute hash process, and hash values used for creating the feature vector space are obtained. The runtime voice-emotion recognition comprises the following steps: extracting utterance features; training a recognition classifier to recognize emotion and intensity levels of the utterance units; and the identified emotion and intensity levels and the quantized extracted feature attributes are fused according to respective weights, and the quantized emotion state of the utterance is calculated.
Apparatus and method for speech-emotion recognition with quantized emotional state