Result Details
Fine-Tuning Self-Supervised Models for Language Identification Using Orthonormal Constraint
CAROFILIS, A.
VANDERREYDT, G.
KHALIL, D.
Madikeri Srikanth, FIT (FIT)
Motlíček Petr, doc. Ing., Ph.D., DCGM (FIT)
SCHUEPBACH, C.
Self-supervised models trained with high linguistic diversity,
such as the XLS-R model, can be effectively fine-tuned for
the language recognition task. Typically, a back-end classifier
followed by statistics pooling layer are added during train-
ing. Commonly used back-end classifiers require a large num-
ber of parameters to be trained, which is not ideal in limited
data conditions. In this work, we explore smaller parame-
ter back-ends using factorized Time Delay Neural Network
(TDNN-F). The TDNN-F architecture is also integrated into
Emphasized Channel Attention, Propagation and Aggregation-
TDNN (ECAPA-TDNN) models, termed ECAPA-TDNN-F,
reducing the number of parameters by 30 to 50% absolute,
with competitive accuracies and no change in minimum cost.
The results show that the ECAPA-TDNN-F can be extended
to tasks where ECAPA-TDNN is suitable. We also test the
effectiveness of a linear classifier and a variant, the Orthonor-
mal linear classifier, previously used in x-vector type systems.
The models are trained with NIST LRE17 data and evalu-
ated on NIST LRE17, LRE22 and the ATCO2 LID datasets.
Both linear classifiers outperform conventional back-ends with
improvements in accuracy between 0.9% and 9.1%
Language Identification, Transformers, Wav2Vec2, fine-tuning, low-resource, out-of-domain,
@inproceedings{BUT193354,
author="PRASAD, A. and CAROFILIS, A. and VANDERREYDT, G. and KHALIL, D. and MADIKERI, S. and MOTLÍČEK, P. and SCHUEPBACH, C.",
title="Fine-Tuning Self-Supervised Models for Language Identification Using Orthonormal Constraint",
booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
year="2024",
pages="11921--11925",
publisher="IEEE Signal Processing Society",
address="Seoul",
doi="10.1109/ICASSP48485.2024.10446751",
isbn="979-8-3503-4485-1",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10446751"
}