A hybrid generative-discriminative approach to speaker diarization
| Authors | |
|---|---|
| Publication date | 2008 |
| Host editors |
|
| Book title | Machine Learning for Multimodal Interaction |
| Book subtitle | 5th International Workshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008 : proceedings |
| ISBN |
|
| ISBN (electronic) |
|
| Series | Lecture Notes in Computer Science |
| Event | 5th Joint Workshop on Machine Learning and Multimodal Interaction (MLMI 2008), Utrecht, the Netherlands |
| Pages (from-to) | 98-109 |
| Publisher | Berlin: Springer |
| Organisations |
|
| Abstract | In this paper we present a sound probabilistic approach to speaker diarization. We use a hybrid framework where a distribution over the number of speakers at each point of a multimodal stream is estimated with a discriminative model. The output of this process is used as input in a generative model that can adapt to a novel test set and perform high accuracy speaker diarization. We manage to deal efficiently with the less common, and therefore harder, segments like silence and multiple speaker parts in a principled probabilistic manner. |
| Document type | Conference contribution |
| Language | English |
| Published at |
https://doi.org/10.1007/978-3-540-85853-9_9
(Final published version)
|
| Permalink to this page | |