NPLDA: A Deep Neural PLDA Model for Speaker Verification

The state-of-art approach for speaker verification consists of a neural network based embedding extractor along with a backend generative model such as the Probabilistic Linear Discriminant Analysis (PLDA). In this work, we propose a neural network approach for backend modeling in speaker recognition. The likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a verification cost. The proposed model, termed as neural PLDA (NPLDA), is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF). The speaker recognition experiments using the NPLDA model are performed on the speaker verificiation task in the VOiCES datasets as well as the SITW challenge dataset. In these experiments, the NPLDA model optimized using the proposed loss function improves significantly over the state-of-art PLDA based speaker verification system.Comment: Published in Odyssey 2020, the Speaker and Language Recognition Workshop (VOiCES Special Session). Link to GitHub Implementation: https://github.com/iiscleap/NeuralPlda. arXiv admin note: substantial text overlap with arXiv:2001.0703

Clustering Algorithm for Enhanced Bibliography Visualization

A Bibliography is a list of books, publications, journals etc., with details such as authors and references. Visualization could be used as a data analysis tool to represent various types of data, analyze huge chunks of data easily and arrive at interesting results. The idea of this project is to provide a medium which eases the combination of bibliography with visualization. Though there are many sources of bibliographic data like the Digital Bibliography and Library Project (DBLP), Citeseer, Google Scholar, none of these data could be used directly for deducing relations between various entities or for visualizing the relationship between related entities. This project aims at providing a web-service that takes user queries as input and retrieves the corresponding data from a local database. Then the web-service applies a clustering algorithm to the retrieved data and then presents the clustered data as XML to the requestor. The user of the system could be any automated program that aims at providing a visual interface to bibliography. One of the main outcomes of this approach would be bringing out the hidden relationship between various related bibliographic entities and making the relationships more obvious and readable than the existing systems

Caste and the Court: Examining Judicial Selection Bias on Bench Assignments on the Indian Supreme Court

This paper is a study on the effect of caste on bench assignments on the Indian Supreme Court. The objective was to determine whether the Chief Justices have historically assigned associate justices to benches based on their individual castes – Brahmin or Non-Brahmin – in order to tilt the bias of the Court in either an elitist (Brahmin) direction or a non-elitist (Non-Brahmin) direction. Based on a probability analysis of panel assignments, I created a new model to determine the extant of castebased judicial selection bias on the Indian Supreme Court. Using a random sample of cases from 1950 to 2000, a two-sample test of proportionality was employed to test whether any bias was present in the Chief Justice’s bench assignments. No caste bias was discovered in either the fifty-year period of the Court or in a smaller data set of cases between 1977 and 2000 (a period after the emergency between 1975 and 1977)

Neural PLDA Modeling for End-to-End Speaker Verification

While deep learning models have made significant advances in supervised classification problems, the application of these models for out-of-set verification tasks like speaker recognition has been limited to deriving feature embeddings. The state-of-the-art x-vector PLDA based speaker verification systems use a generative model based on probabilistic linear discriminant analysis (PLDA) for computing the verification score. Recently, we had proposed a neural network approach for backend modeling in speaker verification called the neural PLDA (NPLDA) where the likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a verification cost. In this paper, we extend this work to achieve joint optimization of the embedding neural network (x-vector network) with the NPLDA network in an end-to-end (E2E) fashion. This proposed end-to-end model is optimized directly from the acoustic features with a verification cost function and during testing, the model directly outputs the likelihood ratio score. With various experiments using the NIST speaker recognition evaluation (SRE) 2018 and 2019 datasets, we show that the proposed E2E model improves significantly over the x-vector PLDA baseline speaker verification system.Comment: Accepted in Interspeech 2020. GitHub Implementation Repos: https://github.com/iiscleap/E2E-NPLDA and https://github.com/iiscleap/NeuralPld

deep-orange and carnation define distinct stages in late endosomal biogenesis in Drosophila melanogaster

Endosomal degradation is severely impaired in primary hemocytes from larvae of eye color mutants of Drosophila. Using high resolution imaging and immunofluorescence microscopy in these cells, products of eye color genes, deep-orange (dor) and carnation (car), are localized to large multivesicular Rab7-positive late endosomes containing Golgi-derived enzymes. These structures mature into small sized Dor-negative, Car-positive structures, which subsequently fuse to form tubular lysosomes. Defective endosomal degradation in mutant alleles of dor results from a failure of Golgi-derived vesicles to fuse with morphologically arrested Rab7-positive large sized endosomes, which are, however, normally acidified and mature with wild-type kinetics. This locates the site of Dor function to fusion of Golgi-derived vesicles with the large Rab7-positive endocytic compartments. In contrast, endosomal degradation is not considerably affected in car1 mutant; fusion of Golgi-derived vesicles and maturation of large sized endosomes is normal. However, removal of Dor from small sized Car-positive endosomes is slowed, and subsequent fusion with tubular lysosomes is abolished. Overexpression of Dor in car1 mutant aggravates this defect, implicating Car in the removal of Dor from endosomes. This suggests that, in addition to an independent role in fusion with tubular lysosomes, the Sec1p homologue, Car, regulates Dor function