Book ChapterDOI
The ICSI RT07s Speaker Diarization System
Chuck Wooters,Marijn Huijbregts +1 more
- pp 509-519
Reads0
Chats0
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
More filters
Journal ArticleDOI
Multimodal speaker diarization for meetings using volume-evaluated SRP-PHAT and video analysis
TL;DR: This article proposes a multimodal speaker diarization system for meeting environments based on a modified SRP-PHAT function evaluated on space volumes rather than discrete points, enabling audio-based localization based on the selection of local maxima.
Proceedings ArticleDOI
Towards automatic speaker retrieval for large multimedia archives
TL;DR: It is shown that with the large scale speaker diarization approach it is possible to perform query-by-example speaker retrieval; to search for audiovisual documents in which a particular person is talking.
Proceedings ArticleDOI
Locality preserving speaker clustering
TL;DR: This paper's speaker clustering experiments clearly show that in the reduced-dimensional LPP subspace, traditional clustering techniques such as k-means and hierarchical clustering perform significantly better than they would in the original high-dimensional GMM mean supervector space and in its principal component subspace.
Journal ArticleDOI
Real-Time Activity Detection in a Multi-Talker Reverberated Environment
TL;DR: A real-time person activity detection framework operating in presence of multiple sources in reverberated environments and designed to automatically reduce the distortions introduced by room reverberation in the available distant speech signals to achieve a significant improvement of speech quality for each speaker.
Development and Evaluation of an Immersive Audio Conferencing System
TL;DR: An immersive audio conferencing system which is able to play back the conference contributions to a remote conferee in a spatially separated manner is developed and a sound acquisition system is constructed that is ableTo achieve HRTF-based 3D sound synthesis of conference contributions.
References
More filters
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.