Speaker diarization using PLDA-based speaker clustering

Prażak, Jan; Silovský, Jan

doi:10.1109/idaacs.2011.6072771

Cited by 16 publications

(13 citation statements)

References 2 publications

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“…The inspirations for our current saga, [13] and [14], also independently led to the work presented in [21], which uses PCA and K-means for two-speaker diarization in a way similar to our methods in [15]. Factor analysis-based features are used in [22] alongside the Cross Likelihood Ratio as a criterion for hierarchical clustering, while [23] performs clustering using PLDA as inspired by its recent success in speaker verification. Moreover, the work in [24] defines the assignment of speech segments-each represented using a factor analysis-based feature vector-to speaker clusters in terms of an Integer Linear Program.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

Shum

Dehak

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

165

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Abstract-In speaker diarization, standard approaches typically perform speaker clustering on some initial segmentation before refining the segment boundaries in a re-segmentation step to obtain a final diarization hypothesis. In this paper, we integrate an improved clustering method with an existing re-segmentation algorithm and, in iterative fashion, optimize both speaker cluster assignments and segmentation boundaries jointly. For clustering, we extend our previous research using factor analysis for speaker modeling. In continuing to take advantage of the effectiveness of factor analysis as a front-end for extracting speaker-specific features (i.e., i-vectors), we develop a probabilistic approach to speaker clustering by applying a Bayesian Gaussian Mixture Model (GMM) to principal component analysis (PCA)-processed i-vectors. We then utilize information at different temporal resolutions to arrive at an iterative optimization scheme that, in alternating between clustering and re-segmentation steps, demonstrates the ability to improve both speaker cluster assignments and segmentation boundaries in an unsupervised manner. Our proposed methods attain results that are comparable to those of a state-of-the-art benchmark set on the multi-speaker CallHome telephone corpus. We further compare our system with a Bayesian nonparametric approach to diarization and attempt to reconcile their differences in both methodology and performance.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

Shum

Dehak

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

165

View full text Add to dashboard Cite

show abstract

“…Clustering i-vectors based on the cosine distance has been widely adopted in diarisation systems (Shum et al, 2011;Sell and Garcia-Romero, 2014;Garcia-Romero and Espy-Wilson, 2011;Senoussaoui et al, 2014). Furthermore, variational Bayes methods are often used to refine the i-vector-based clustering results or segment boundaries (Kenny et al, 2010;Prazak and Silovsky, 2011;Shum et al, 2013;Sell and Garcia-Romero, 2015).…”

Section: Related Workmentioning

confidence: 99%

Combination of Deep Speaker Embeddings for Diarisation

Sun,

Zhang,

Woodland

2020

Preprint

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Combining deep speaker embedding (window-level d-vector) extraction systems using 2D self-attentive, gated additive and bilinear pooling methods. The best-performing structure for combination is obtained by stacking a 2D self-attentive and a bilinear pooling structures.• A complete single pass neural network-based diarisation pipeline is introduced, which includes neural voice activity detection, neural change point detection, a deep speaker embedding extraction system and spectral clustering.• Experiments on both AMI and NIST RT05 evaluation sets showed that our proposed methods can produce state-ofthe-art results for the very challenging multi-speaker (with 4-10 speakers) meeting diarisation task.• We use the AMI dataset based on the official speech recognition partition with the audios recorded by multiple distance microphones (MDM) since it is a more realistic setup for meeting transcription than many different setups used by previous AMI based studies. This is the first paper to use this setup to the best of the authors' knowledge. Although this makes our results not directly comparable to those from the previous papers, we think our system still shows superior performance, since the results shown in Table 11 are the lowest diarisation error rates with the same training data, and the realistic setup we used can increase the difficulty of the task.• This paper is made based on our previous conference paper (Sun et al., 2019). Compared to our previous paper -Almost all of the combination structures are newly proposed and not included in (Sun et al., 2019), apart from the first type of 2D self-attentive method defined in Eqn. (4).-Sun et al. ( 2019) only performed the experiments on the AMI data with manual segmentation, while this paper explores to use both manual and automatic segmentation. The neural diarisation pipeline is newly introduced in this paper, although the neural VAD structure was proposed in (Wang et al., 2016).

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“…Posed as an unsupervised learning problem, its success relies heavily on the choice of an appropriate distance metric for performing clustering. While classical approaches [2,3,4] resorted to careful feature design coupled with a predefined metric, This work was supported in part by the ASU SenSIP center, Arizona State University. Portions of this work were performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.…”

Section: Introductionmentioning

confidence: 99%

Designing an Effective Metric Learning Pipeline for Speaker Diarization

Narayanaswamy¹,

Thiagarajan²,

Song³

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the appropriate feature extractor, ranging from pre-trained i−vectors to representations learned via different sequence modeling architectures (e.g. 1D-CNNs, LSTMs, attention models), while adopting off-the-shelf metric learning solutions. In this paper, we argue that, regardless of the feature extractor, it is crucial to carefully design a metric learning pipeline, namely the loss function, the sampling strategy and the discrimnative margin parameter, for building robust diarization systems. Furthermore, we propose to adopt a fine-grained validation process to obtain a comprehensive evaluation of the generalization power of metric learning pipelines. To this end, we measure diarization performance across different language speakers, and variations in the number of speakers in a recording. Using empirical studies, we provide interesting insights into the effectiveness of different design choices and make recommendations.

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Speaker diarization using PLDA-based speaker clustering

Cited by 16 publications

References 2 publications

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

Combination of Deep Speaker Embeddings for Diarisation

Designing an Effective Metric Learning Pipeline for Speaker Diarization

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