A Study of x-Vector 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 variations when compared to the dataset mismatch between in-and out-domain data.

X-Vectors Meet Emotions: A Study On Dependencies Between Emotion and Speaker Recognition

“…1(c)), EERs are better for the former, which could be because phonetic content variability limited to only 12 sentences. For comparison, the authors in [30] report EER increasing from 2.5% to more than 20% when going from full-length recordings to 5sec versus 5sec trials on NIST 2010 corpora.…”

Quality measures for speaker verification with short utterances

“…State-of-the-art ASV systems exhibit satisfactory performance with adequately long ( 2 minutes) speech data. However, reduction in amount of speech drastically degrades the ASV performance [10,12,18,19,20]. The requirement of sufficiently long speech for training or testing, especially in presence of large intersession variability has limited the potential of widespread real-world implementations.…”

Defense Against Adversarial Attacks on Spoofing Countermeasures of ASV

“…ASV is undisputedly a crucial technology for biometric identification, which is broadly applied in real-world applications like banking and home automation. Considerable performance improvements in terms of both accuracy and efficiency of ASV systems have been achieved through active research in a diversity of approaches [1][2][3][4][5][6]. [4] proposed a method that use the Gaussian mixture model to extract acoustic features and then apply the likelihood ratio for scoring.…”