Domain Adaptation Based Speaker Recognition On Short Utterances

Abstract

This paper explores how the in- and out-domain probabilistic linear discriminant analysis (PLDA) speaker verification behave when enrolment and verification lengths are reduced. Experiment studies have found that when full-length utterance is used for evaluation, in-domain PLDA approach shows more than 28% improvement in EER and DCF values over out-domain PLDA approach and when short utterances are used for evaluation, the performance gain of in-domain speaker verification reduces at an increasing rate. Novel modified inter dataset variability (IDV) compensation is used to compensate the mismatch between in- and out-domain data and IDV-compensated out-domain PLDA shows respectively 26% and 14% improvement over out-domain PLDA speaker verification when SWB and NIST data are respectively used for S normalization. When the evaluation utterance length is reduced, the performance gain by IDV also reduces as short utterance evaluation data i-vectors have more variations due to phonetic varia

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykanagasundaram2016domain

Related papers

• A Generalized Framework For Domain Adaptation Of PLDA In Speaker Recognition (2020)7.50
• Improving Short Utterance PLDA Speaker Verification Using SUV Modelling And Utterance Partitioning Approach (2016)0.00
• DNN Based Speaker Recognition On Short Utterances (2016)0.00
• Local Training For PLDA In Speaker Verification (2016)0.00
• Multiobjective Optimization Training Of PLDA For Speaker Verification (2018)2.26
• Generalized Domain Adaptation Framework For Parametric Back-end In Speaker Recognition (2023)0.00
• Squeezing Value Of Cross-domain Labels: A Decoupled Scoring Approach For Speaker Verification (2020)0.00
• End-to-end DNN Based Speaker Recognition Inspired By I-vector And PLDA (2017)10.35