Language-depedent I-Vectors for LRE15
Niko Br\"ummer, Albert Swart

TL;DR
This paper introduces an alternative method for propagating uncertainty in i-vector based spoken language recognition, addressing limitations of the standard Gaussian backend especially for short utterances.
Contribution
It presents a novel approach to incorporate i-vector uncertainty into the backend, improving language recognition accuracy over existing methods.
Findings
Enhanced recognition performance for short utterances
Effective uncertainty propagation in i-vector systems
Improved robustness over standard Gaussian backend
Abstract
A standard recipe for spoken language recognition is to apply a Gaussian back-end to i-vectors. This ignores the uncertainty in the i-vector extraction, which could be important especially for short utterances. A recent paper by Cumani, Plchot and Fer proposes a solution to propagate that uncertainty into the backend. We propose an alternative method of propagating the uncertainty.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpeech Recognition and Synthesis · Speech and Audio Processing · Speech and dialogue systems
