Abstract
In psychology field research, experts generally design a standard experimental procedure, e.g., consultation, show or talk, to observe the mental state of human. They expect to trigger reactions of internal emotion by stimulating external behavior. However, when analyzing whole interaction process, different lengths of fragments of interaction including different strength of emotional information, and experts make more complete and suitable decision. Our work inspired by the conception and apply it on automatic behavior rating system of couple therapy database, to improve the accuracy of scoring interaction process of psychotherapy. This program recruit seriously and chronically distressed married couples, and let them make a problem-solving communication for specific topic, recording the audio, video and text of process, experts analyze the extent of behavior of couples interaction process to evaluate treatment effects by these information. This paper use Bidirectional Long Short Term Memory structure to extract multi- granular and high-level features for lexical modality, also combine Doc2Vec into document level with feature selection to integrate different temporal level of behavioral features, and finally join audio modality to train binary classifier with machine learning algorithm. For the performance of six behavioral codes, husband and wife's average accuracy of behavior achieve 79.3% and 82.4% separately, this enhance 5.3% and 7.4% average accuracy compared to 74% and 75% of previous paper[1]. Our experiments and results present the merit of use of Bidirectional Long Short Term Memory can learn time series information effectively, the computation of different level granularity of intensity of behavior improving the algorithm on couple therapy rating system.