Using long short-term memory recurrent neural network for speaker diarization segmentation
US10249292B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 14, 2016 |
| Grant date | Apr 2, 2019 |
| Priority date | — |
| Expiry date | Feb 4, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L25/78
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Speaker diarization is performed on audio data including speech by a first speaker, speech by a second speaker, and silence. The speaker diarization includes segmenting the audio data using a long short-term memory (LSTM) recurrent neural network (RNN) to identify change points of the audio data that divide the audio data into segments. The speaker diarization includes assigning a label selected from a group of labels to each segment of the audio data using the LSTM RNN. The group of labels comprising includes labels corresponding to the first speaker, the second speaker, and the silence. Each change point is a transition from one of the first speaker, the second speaker, and the silence to a different one of the first speaker, the second speaker, and the silence. Speech recognition can be performed on the segments that each correspond to one of the first speaker and the second speaker.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.