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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Speaker diarization of spontaneous meeting room conversations
TL;DR: New features based on structure of a conversation such as silence and speaker change statistics for overlap detection and different artificial neural network architectures to extract speaker discriminant features and use these features as input to speaker diarization systems are proposed.
Posted Content
Multilayer bootstrap network for unsupervised speaker recognition
TL;DR: In this paper, a multilayer bootstrap network (MBN) was applied to unsupervised speaker recognition by clustering the low-dimensional data, and the results demonstrated the effectiveness and robustness of the proposed method.
Proceedings ArticleDOI
Universal Background Sparse Coding and Multilayer Bootstrap Network for Speaker Clustering.
TL;DR: This work applies multilayer bootstrap network (MBN) to speaker clustering and proposes an MBN-based universal background model, named universal background sparse coding, which demonstrates the effectiveness and robustness of the proposed method.
Journal ArticleDOI
Narrative theme navigation for sitcoms supported by fan-generated scripts
TL;DR: The article describes the original and the extended Joke-O-Mat system, discusses problems with the use of fan-generated content, and presents results on episodes from the sitcom Seinfeld with regards to segmentation accuracy and overall user satisfaction as determined by a human-subject study.
Dissertation
Computers to help with conversations : affective framework to enhance human nonverbal skills
TL;DR: The authors developed My Automated Conversation coacH (MACH), a real-time system that provides ubiquitous access to social skills training, which includes a virtual agent that reads facial expressions, speech, and prosody, and responds with verbal and nonverbal behaviors in real time.
References
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Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion
TL;DR: The segmentation algorithm can successfully detect acoustic changes; the clustering algorithm can produce clusters with high purity, leading to improvements in accuracy through unsupervised adaptation as much as the ideal clustering by the true speaker identities.
Proceedings ArticleDOI
A robust speaker clustering algorithm
Jitendra Ajmera,Chuck Wooters +1 more
TL;DR: The algorithm automatically performs both speaker segmentation and clustering without any prior knowledge of the identities or the number of speakers and has the following advantages: no threshold adjustment requirements; no need for training/development data; and robustness to different data conditions.
Proceedings ArticleDOI
Approaches and applications of audio diarization
TL;DR: An overview of current audio diarization approaches is provided and performance and potential applications are discussed, as well as the performance of current systems as measured in the DARPA EARS Rich Transcription Fall 2004 (RT-04F) speaker diarized evaluation.
Journal ArticleDOI
Robust speaker change detection
TL;DR: In this article, the authors present a criterion which can be used to identify speaker changes in an audio stream without such tuning, which consists of calculating the log likelihood ratio (LLR) of two models with the same number of parameters.