Effects of Gender Information in Text-Independent and Text-Dependent Speaker Verification
Self archived versionfinal draft
MetadataShow full item record
CitationKanervisto, Anssi. Sahidullah, Md. Vestman, Ville. Hautamäki, Ville. Kinnunen, Tomi. (2017). Effects of Gender Information in Text-Independent and Text-Dependent Speaker Verification. Proceedings of The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), 1520-6149. 10.1109/ICASSP.2017.7953180.
It is well-known that for speaker recognition task, gender-dependent acoustic modeling performs better than gender-independent modeling. The practice is to use the gender ground-truth and to train gender-dependent models. However, such information is not necessarily available, especially if speakers are remotely enrolled. A way to overcome this is to use a gender classification system, which introduces an additional layer of uncertainty. To date, such uncertainty has not been studied. We implement two gender classifier systems and test them with two different corpora and speaker verification systems. We find that estimated gender information can improve speaker verification accuracy over gender-independent methods. Our detailed analysis suggests that gender estimation should have a sufficiently high accuracy to yield improvements in speaker verification performance.