Speech personality recognition relies on training models that require an excessive number of features and are, in most cases, designed specifically for certain databases. As a result, when tested on different datasets, overfitted classifier models are not always reliable because their accuracy changes with changes in the domain of speakers. Moreover, personality annotations are often subjective, which creates variability in raters perception during labeling. These problems inhibit the effectiveness of speech personality recognition applications. To reduce the unexplained variance caused by unknown differences in raters perception, a structure that uses Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm is proposed. Furthermore, a feature extraction method is proposed to filter out undesirable adulterations be it noise, silence, or uncertain pitch segments, while extracting essential audio features, i.e., signal power roll-off, pitch, and pause rate. Experiments on the standard SSPNet dataset records a relative 4% increase in overall accuracy when log-likelihood based annotations are used. Moreover, improved consistency in accuracy is observed when this method is tested on male and female subsets.

To ensure that people can supplement enough water at the right time, an initiative service method based on intention understanding for drinking service robot is proposed. Firstly, individual factors and environmental factors which are associated with personal intentions are collected. Secondly, users' intention and desire degree for intention are obtained by an improved Fuzzy-Context-specific Intention Inference method. Then the relationship between personal intention and human demands is established by an initiative service model. Finally, robots provide drinking water service initiatively according to humans' demands. Drinking service experiments are performed in a laboratory scenario using a humans-robots interaction system. The experimental results show that the drinking time of actual situation lags behind the initiative service time 25 minutes on average, and the initiative service renders drinking service 2 times more than actual situation on average, which demonstrates the feasibility of the proposal. In prospect, the initiative service method could be applied to many occasions in our daily life, e.g., family service, caring for the elderly, and medical rehabilitation.