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
Posted Content
Speaker Diarization with Region Proposal Network
Zili Huang,Shinji Watanabe,Yusuke Fujita,Paola Garcia,Yiwen Shao,Daniel Povey,Sanjeev Khudanpur +6 more
TL;DR: This paper proposes a novel speaker diarization method: Region Proposal Network based Speaker Diarization (RPNSD), where a neural network generates overlapped speech segment proposals, and compute their speaker embeddings at the same time.
Journal ArticleDOI
Active Learning Based Constrained Clustering For Speaker Diarization
Chengzhu Yu,John H. L. Hansen +1 more
TL;DR: The results indicate that the proposed active learning algorithms are able to reduce diarization error rate significantly with a relatively small amount of human supervision.
Book ChapterDOI
Multi-stage Speaker Diarization for Conference and Lecture Meetings
TL;DR: The LIMSI RT-07S speaker diarization system for the conference and lecture meetings is presented in this article, which combines agglomerative clustering based on Bayesian information criterion (BIC) with a second clustering using state-of-the-art speaker identification (SID) techniques.
Proceedings ArticleDOI
Speaker diarization using unsupervised discriminant analysis of inter-channel delay features
TL;DR: A novel and nonetheless unsupervised approach to the discriminant analysis of delay features for speaker diarization that aims to increase speaker separability in delay-space.
Patent
Neural network-based speech processing
TL;DR: In this paper, a neural network is trained using the feature vectors and an objective function that induces the network to classify whether the speech samples come from the same speaker, and the weights from the tied weight matrix are extracted for use in generating derived features for a speech processing system that can benefit from features that are thus transformed to better reflect speaker identity.
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.