Graph2Speak: Improving Speaker Identification using Network Knowledge in Criminal Conversational Data
| Type of publication: | Conference paper |
| Citation: | Sarfjoo_SPSC_2021 |
| Publication status: | Published |
| Booktitle: | 1st ISCA Symposium on Security and Privacy in Speech Communication |
| Year: | 2021 |
| Pages: | 10--13 |
| Crossref: | Fabien_Idiap-RR-01-2023: |
| DOI: | 10.21437/SPSC.2021-3 |
| Abstract: | Criminal investigations mostly rely on the collection of speech conversational data in order to identify speakers and build or enrich an existing criminal network. Social network analysis tools are then applied to identify the central characters and the different communities within the network. This paper introduces a new method, Graph2Speak, to re-rank individuals after applying a speaker identification step, by leveraging the frequency of previous interactions extracted from a graph. We deploy our method on two candidate datasets for criminal conversational data, Crime Scene Investigation (CSI), a television show, and the ROXANNE simulated data. We demonstrate that our method can reduce the error rates of the speaker identification baseline by up to 12% (relative). |
| Keywords: | |
| Projects |
Idiap EC H2020-ROXANNE |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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