Name
Abstract | ||
|---|---|---|
Conversation analysis plays an important role in social psychology, interpersonal relationship management, and human-care computing. However, few of existing studies considers speech contents for effective conversation partner grouping (abbr. CPG). In this paper, we propose a new framework for conversation partner grouping based on speech contents. Under the proposed framework, we propose two novel algorithms for effective CPG, called CPG-LDA and CPG-LSI, respectively. Both of them use voice recognition tools to convert audio-based speech data into text-based speech contents, and then apply topic modeling and k-means algorithms for CPG. However, the former is based on LDA topic modeling, while the latter is LSI. The experiments show that both CPG-LDA and CPG-LSI have good performance for GPC. More impressively, the proposed CPG-LSI algorithm archives up to 95.83% recognition rate in the experiments. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCE-TW46550.2019.8991695 | ICCE-TW |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Hsine Lin | 1 | 0 | 0.34 |
| JianTao Huang | 2 | 0 | 0.34 |
| Yi-Ching Lyu | 3 | 0 | 0.34 |
| Po-Chuan Huang | 4 | 0 | 0.34 |
| Cheng-Wei Wu | 5 | 6 | 2.84 |