13,968 research outputs found
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
A Speaker Diarization System for Studying Peer-Led Team Learning Groups
Peer-led team learning (PLTL) is a model for teaching STEM courses where
small student groups meet periodically to collaboratively discuss coursework.
Automatic analysis of PLTL sessions would help education researchers to get
insight into how learning outcomes are impacted by individual participation,
group behavior, team dynamics, etc.. Towards this, speech and language
technology can help, and speaker diarization technology will lay the foundation
for analysis. In this study, a new corpus is established called CRSS-PLTL, that
contains speech data from 5 PLTL teams over a semester (10 sessions per team
with 5-to-8 participants in each team). In CRSS-PLTL, every participant wears a
LENA device (portable audio recorder) that provides multiple audio recordings
of the event. Our proposed solution is unsupervised and contains a new online
speaker change detection algorithm, termed G 3 algorithm in conjunction with
Hausdorff-distance based clustering to provide improved detection accuracy.
Additionally, we also exploit cross channel information to refine our
diarization hypothesis. The proposed system provides good improvements in
diarization error rate (DER) over the baseline LIUM system. We also present
higher level analysis such as the number of conversational turns taken in a
session, and speaking-time duration (participation) for each speaker.Comment: 5 Pages, 2 Figures, 2 Tables, Proceedings of INTERSPEECH 2016, San
Francisco, US
Jitter and Shimmer measurements for speaker diarization
Jitter and shimmer voice quality features have been successfully
used to characterize speaker voice traits and detect voice pathologies.
Jitter and shimmer measure variations in the fundamental frequency
and amplitude of speaker's voice, respectively. Due to their nature, they can be used to assess differences between speakers. In this paper, we investigate the usefulness of these voice quality features in the task of speaker diarization. The combination of voice quality features with the conventional spectral features, Mel-Frequency Cepstral Coefficients (MFCC), is addressed in the framework of Augmented Multiparty Interaction (AMI) corpus, a multi-party and spontaneous speech set of recordings. Both sets of features are independently modeled using mixture of Gaussians and fused together at the score likelihood level. The experiments carried out on the AMI corpus show that incorporating jitter and shimmer measurements to the baseline spectral features decreases the diarization error rate in most of the recordings.Peer ReviewedPostprint (published version
A sticky HDP-HMM with application to speaker diarization
We consider the problem of speaker diarization, the problem of segmenting an
audio recording of a meeting into temporal segments corresponding to individual
speakers. The problem is rendered particularly difficult by the fact that we
are not allowed to assume knowledge of the number of people participating in
the meeting. To address this problem, we take a Bayesian nonparametric approach
to speaker diarization that builds on the hierarchical Dirichlet process hidden
Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006)
1566--1581]. Although the basic HDP-HMM tends to over-segment the audio
data---creating redundant states and rapidly switching among them---we describe
an augmented HDP-HMM that provides effective control over the switching rate.
We also show that this augmentation makes it possible to treat emission
distributions nonparametrically. To scale the resulting architecture to
realistic diarization problems, we develop a sampling algorithm that employs a
truncated approximation of the Dirichlet process to jointly resample the full
state sequence, greatly improving mixing rates. Working with a benchmark NIST
data set, we show that our Bayesian nonparametric architecture yields
state-of-the-art speaker diarization results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS395 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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