The purpose of this work is to show how recent developments in cepstral-based systems for speaker recognition can be leveraged.
PDF | On Aug 27, 2011, Nicolas Scheffer and others published Factor Analysis Back Ends for MLLR Transforms in Speaker Recognition.
Factor Analysis Back Ends for MLLR Transforms in Speaker Recognition. Scheffer, N., Lei, Y., & Ferrer, L. In Proc. Interspeech, Florence, Italy, August ...
Factor analysis back ends for MLLR transforms in speaker recognition. Scheffer N., Lei Y., Ferrer L. Expand. Publication type: Proceedings Article.
Factor Analysis Back Ends for MLLR Transforms in Speaker Recognitionmore. by Nicolas Scheffer · Download (.pdf). •. Towards noise-robust speaker recognition ...
Aug 23, 2022 · Ferrer (2011), Factor analysis back ends for MLLR transforms in speaker recognition, Proc. Interspeech, pp. 257-260, Florence. M. Kockmann ...
Ferrer, “Factor analysis back ends for MLLR transforms in speaker recognition,” in Proc. of In- terspeech 2011, 2011, pp. 257–260. [11] P. Comon ...
In this paper, we propose a speaker-verification system based on maximum likelihood linear regression (MLLR) super-vectors, for which speakers are characterized ...
Principal component analysis is performed on the polynomial feature space, and the features are projected onto the subspace spanned by the background ...
People also ask
What are the components of speaker recognition?
What are the various speaker recognition techniques explain?
This paper describes a speaker recognition system based on feature extraction utilizing the constrained maximum likelihood linear regression (CMLLR) speaker ...