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
Combination of deep speaker embeddings for diarisation.
TL;DR: A neural-based single-pass speaker diarisation pipeline is proposed in this paper, which uses NNs to achieve voice activity detection, speaker change point detection, and speaker embedding extraction.
Proceedings ArticleDOI
Parallelizing Speaker-Attributed Speech Recognition for Meeting Browsing
TL;DR: The underlying parallel speaker diarization and speech recognition realizations are presented, a comparison of results based on NIST RT07 evaluation data, and a description of the final application are presented.
Journal ArticleDOI
Speech information retrieval: a review
Ryan P. Hafen,Michael J. Henry +1 more
TL;DR: The goal is to introduce enough background for someone new in the field to quickly gain high-level understanding and to provide direction for further study in each of the major speech analysis fields.
Dissertation
Speaker diarization and tracking in multiple-sensor environments
TL;DR: This thesis work describes a novel speaker verification algorithm that makes use of adapted features from automatic speech recognition, and seeks to improve traditional modeling approaches for speaker identification and verification, through multi-decision and multi-channel processing strategies, in smart-room scenario.
An Information Theoretic Approach to Speaker Diarization of Meeting Recordings
TL;DR: A non parametric approach to speaker diarization for meeting recordings based on an information theoretic framework using the Information Bottleneck principle, which achieves similar speaker error rates as compared to a baseline HMM/GMM system.
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.