DISTBIC: A speaker-based segmentation for audio data indexing
By Perrine Delacourt, Christian J. Wellekens. Speech Communication, 2000
295 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/5c3122dddb34ff2f1120d283a2a4d927620753e5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Shinji Watanabe
- Ahmad Salman
- Naoyuki Kanda
- Kyu J. Han
- Shrikanth S. Narayanan
- Dimitrios Dimitriadis
- Tae Jin Park
- Javier Ortega-Garcia
In Signal Processing
- Shinji Watanabe
- Ahmad Salman
- Naoyuki Kanda
- Kyu J. Han
- Shrikanth S. Narayanan
- Dimitrios Dimitriadis
- Tae Jin Park
- Javier Ortega-Garcia
Citations in the corpus
- A Review of Speaker Diarization: Recent Advances with Deep Learning (2021, methodology): “Among these criteria, BIC has been the most widely used method followed by numerous variants [115, 116, 117, 118].”
- Learning Speaker-Specific Characteristics With a Deep Neural Architecture (2011, methodology): “, false alarm rate (FAR), miss detection rate (MDR) and measure [85],”
- A regularized kernel-based approach to unsupervised audio segmentation (2009, methodology): “A related application is speaker change de t ction, also called speaker diarization, which is generally handle d through parametric modelling based on Gaussian Mixture Models (GMM s) [4].”
- A Tutorial on Text-Independent Speaker Verification (2004, background): “The aim of the segmentation task is to determine the number of speakers and when they speak [53, 54, 55, 56, 57, 58, 59].”