Book ChapterDOI
The ICSI RT07s Speaker Diarization System
Chuck Wooters,Marijn Huijbregts +1 more
- pp 509-519
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TLDR
The ICSI speaker diarization system as mentioned in this paper automatically performs both speaker segmentation and clustering without any prior knowledge of the identities or the number of speakers, using standard speech processing components and techniques such as HMMs, agglomerative clustering, and the Bayesian Information Criterion.Abstract:
In this paper, we present the ICSI speaker diarization system. This system was used in the 2007 National Institute of Standards and Technology (NIST) Rich Transcription evaluation. The ICSI system automatically performs both speaker segmentation and clustering without any prior knowledge of the identities or the number of speakers. Our system uses "standard" speech processing components and techniques such as HMMs, agglomerative clustering, and the Bayesian Information Criterion. However, we have developed the system with an eye towards robustness and ease of portability. Thus we have avoided the use of any sort of model that requires training on "outside" data and we have attempted to develop algorithms that require as little tuning as possible.
The system is simular to last year's system [1] except for three aspects. We used the most recent available version of the beam-forming toolkit, we implemented a new speech/non-speech detector that does not require models trained on meeting data and we performed our development on a much larger set of recordings.read more
Citations
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Journal ArticleDOI
Speaker Diarization: A Review of Recent Research
Xavier Anguera Miro,Simon Bozonnet,Nicholas Evans,Corinne Fredouille,Gerald Friedland,Oriol Vinyals +5 more
TL;DR: An analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research are presented.
Proceedings ArticleDOI
An HDP-HMM for systems with state persistence
TL;DR: A sampling algorithm is developed that employs a truncated approximation of the DP to jointly resample the full state sequence, greatly improving mixing rates and demonstrating the advantages of the sticky extension, and the utility of the HDP-HMM in real-world applications.
Book ChapterDOI
Bayesian Nonparametrics: Hierarchical Bayesian nonparametric models with applications
Yee Whye Teh,Michael I. Jordan +1 more
TL;DR: The role of hierarchical modeling in Bayesian nonparametrics is discussed, focusing on models in which the infinite-dimensional parameters are treated hierarchically, and the value of these hierarchical constructions is demonstrated in a wide range of practical applications.
Journal ArticleDOI
Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language
TL;DR: Behavioral informatics applications of these signal processing techniques that contribute to quantifying higher level, often subjectively described, human behavior in a domain-sensitive fashion are illustrated.
A sticky HDP-HMM with application to speaker diarization
TL;DR: An augmented HDP-HMM is described that provides effective control over the switching rate and makes it possible to treat emission distributions nonparametrically, and a sampling algorithm is developed that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence.
References
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Journal Article
Robust speaker diarization for meetings : ICSI RT06s meetings evaluation system
TL;DR: The ICSI speaker diarization system submitted for the NIST Rich Transcription evaluation (RT06s) conducted on the meetings environment as discussed by the authors is based on the RT05s system, which uses agglomerative clustering with a modified Bayesian information criterion (BIC) measure to decide which pairs of clusters to merge and to determine when to stop merging clusters.
Proceedings ArticleDOI
Filtering the Unknown: Speech Activity Detection in Heterogeneous Video Collections
TL;DR: The speech activity detection system that was used for detecting speech regions in the Dutch TRECVID video collection is discussed, designed to filter non-speech like music or sound effects out of the signal without the use of predefined non- speech models.
Journal Article
Speech-based Annotation of Heterogeneous Multimedia Content Using Automatic Speech Recognition
TL;DR: The setup and evaluation of robust speech recognition system parts, geared towards transcript generation for heterogeneous, real-life media collections, for NIST/TRECVID-2007 test collection are reported on.