Browsing by Author "Chetupalli, S.R."

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Comparison of low-dimension speech segment embeddings: Application to speaker diarization
(2019) Chetupalli, S.R.; Sreenivas, T.V.; Gopalakrishnan, A.
Segment clustering is a crucial step in unsupervised speaker diarization. Bottom-up approaches, such as, hierarchical agglomerative clustering technique are used traditionally for segment clustering. In this paper, we consider the top-down approach to clustering, in which a speaker sensitive, low-dimensional representation of segments (speaker space) is obtained first, followed by Gaussian mixture model (GMM) based clustering. We explore three methods of obtaining the low dimension segment representation: (i) multi-dimensional scaling (MDS) based on segment to segment stochastic distances; (ii) traditional principal component analysis (PCA), and (iii) factor analysis (i-vectors), of GMM mean super-vectors. We found that, MDS based embeddings result in better representation and hence result in better diarization performance compared to PCA and even i-vector embeddings. � 2019 IEEE.
Comparison of low-dimension speech segment embeddings: Application to speaker diarization
(Institute of Electrical and Electronics Engineers Inc., 2019) Chetupalli, S.R.; Sreenivas, T.V.; Gopalakrishnan, A.
Segment clustering is a crucial step in unsupervised speaker diarization. Bottom-up approaches, such as, hierarchical agglomerative clustering technique are used traditionally for segment clustering. In this paper, we consider the top-down approach to clustering, in which a speaker sensitive, low-dimensional representation of segments (speaker space) is obtained first, followed by Gaussian mixture model (GMM) based clustering. We explore three methods of obtaining the low dimension segment representation: (i) multi-dimensional scaling (MDS) based on segment to segment stochastic distances; (ii) traditional principal component analysis (PCA), and (iii) factor analysis (i-vectors), of GMM mean super-vectors. We found that, MDS based embeddings result in better representation and hence result in better diarization performance compared to PCA and even i-vector embeddings. Â© 2019 IEEE.
Feature selection and model optimization for semi-supervised speaker spotting
(2016) Chetupalli, S.R.; Gopalakrishnan, A.; Sreenivas, T.V.
We explore, experimentally, feature selection and optimization of stochastic model parameters for the problem of speaker spotting. Based on an initially identified segment of speech of a speaker, an iterative model refinement method is developed along with a latent variable mixture model so that segments of the same speaker are identified in a long speech record. It is found that a GMM with moderate number of mixtures is better suited for the task than a large number mixture model as used in speaker identification. Similarly, a PCA based low-dimensional projection of MFCC based feature vector provides better performance. We show that about 6 seconds of initially identified speaker data is sufficient to achieve > 90% performance of speaker segment identification. � 2016 IEEE.
Feature selection and model optimization for semi-supervised speaker spotting
(European Signal Processing Conference, EUSIPCO, 2016) Chetupalli, S.R.; Gopalakrishnan, A.; Sreenivas, T.V.
We explore, experimentally, feature selection and optimization of stochastic model parameters for the problem of speaker spotting. Based on an initially identified segment of speech of a speaker, an iterative model refinement method is developed along with a latent variable mixture model so that segments of the same speaker are identified in a long speech record. It is found that a GMM with moderate number of mixtures is better suited for the task than a large number mixture model as used in speaker identification. Similarly, a PCA based low-dimensional projection of MFCC based feature vector provides better performance. We show that about 6 seconds of initially identified speaker data is sufficient to achieve > 90% performance of speaker segment identification. Â© 2016 IEEE.