ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Supervised i-vector Modeling - Theory and Applications

Ramoji, Shreyas and Ganapathy, Sriram (2019) Supervised i-vector Modeling - Theory and Applications. In: 19th Annual Conference of the International Speech Communication, INTERSPEECH 2018;, 2-6 Sept., 2018, Hyderabad International Convention Centre (HICC)Hyderabad, pp. 1091-1095.

[img] PDF
Interspeech(4).pdf - Published Version
Restricted to Registered users only

Download (290kB) | Request a copy

Abstract

Over the last decade, the factor analysis based modeling of a variable length speech utterance into a fixed dimensional vector (termed as i-vector) has been prominently used for many tasks like speaker recognition, language recognition and even in speech recognition. The i-vector model is an unsupervised learning paradigm where the data is initially clustered using a Gaussian Mixture Universal Background Model (GMM-UBM). The adapted means of the Gaussian mixture components are dimensionality reduced using the Total Variability Matrix (TVM) where the latent variables are modeled with a single Gaussian distribution. In this paper, we propose to rework the theory of i-vector modeling using a supervised framework where the speech utterances are associated with a label. Class labels arc introduced in the i-vector model using a mixture Gaussian prior. We show that the proposed model is a generalized i-vector model and the conventional i-vector model turns out to be a special case of this model. This model is applied for a language recognition task using the NIST Language Recognition Evaluation (LRE) 2017 dataset. In these experiments, the supervised i-vector model provides significant improvements over the conventional i-vector model (average relative improvements of 5 % in terms of C-avg).

Item Type: Conference Proceedings
Series.: Interspeech
Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC
Additional Information: 19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018), Hyderabad, INDIA, AUG 02-SEP 06, 2018
Keywords: Supervised Expectation Maximization; Total Variability Matrix; i-vector Modeling; Gaussian Back-end
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 12 Mar 2020 10:23
Last Modified: 12 Mar 2020 10:23
URI: http://eprints.iisc.ac.in/id/eprint/62918

Actions (login required)

View Item View Item