61 research outputs found

    Latent Class Model with Application to Speaker Diarization

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    In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

    Latent Class Model with Application to Speaker Diarization

    Get PDF
    In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny’s variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. (1) Adding neighbor windows to extract more speaker information for each short segment. (2) Using a hidden Markov model to avoid frequent speaker change points. (3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00, and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

    Idiopathic Ventricular Arrhythmias Originating From the Pulmonary Sinus Cusp Prevalence, Electrocardiographic/Electrophysiological Characteristics, and Catheter Ablation

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    AbstractBackgroundIdiopathic ventricular arrhythmias (VAs) originating from the pulmonary sinus cusp (PSC) have not been sufficiently clarified.ObjectivesThe goal of this study was to investigate the prevalence, electrocardiographic characteristics, mapping, and ablation of idiopathic VAs arising from the PSC.MethodsData were analyzed from 218 patients undergoing successful endocardial ablation of idiopathic VAs with a left bundle branch block morphology and inferior axis deviation.ResultsTwenty-four patients had VAs originating from the PSC. In the first 7 patients, initial ablation performed in the right ventricular outflow tract failed to abolish the clinical VAs but produced a small change in the QRS morphology in 3 patients. In all 24 patients, the earliest activation was eventually identified in the PSC, at which a sharp potential was observed preceding the QRS complex onset by 28.2 ± 2.9 ms. The successful ablation site was in the right cusp (RC) in 10 patients (42%), the left cusp (LC) in 8 (33%), and the anterior cusp (AC) in 6 (25%). Electrocardiographic analysis showed that RC-VAs had significantly larger R-wave amplitude in lead I and a smaller aVL/aVR ratio of Q-wave amplitude compared with AC-VAs and LC-VAs, respectively. The R-wave amplitude in inferior leads was smaller in VAs localized in the RC than in the LC but did not differ between VAs from the AC and LC.ConclusionsVAs arising from the PSC are not uncommon, and RC-VAs have unique electrocardiographic characteristics. These VAs can be successfully ablated within the PSC
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