The effect of voice disguise on Automatic Speaker Recognition

Tiejun Tan

Found an issue? Give us feedback

https://doi.org/10.1...arrow_drop_down

https://doi.org/10.1109/cisp.2...

Article . 2010 . Peer-reviewed

Data sources: Crossref

https://dx.doi.org/10.1109/cis...

Article

Data sources: Microsoft Academic Graph

The effect of voice disguise on Automatic Speaker Recognition

descriptionPublicationkeyboard_double_arrow_right Article 01 Oct 2010Publisher:IEEEJournal:2010 3rd International Congress on Image and Signal Processing

Authors: Tiejun Tan;

doi: 10.1109/cisp.2010.5647131

The effect of voice disguise on Automatic Speaker Recognition

- Summary
- Metrics

Abstract

In this paper the effect of 10 kinds of disguise voices on the performance of a Forensic Automatic Speaker Recognition System (FASRS) was studied. 10 types of disguised voices and normal voices from 20 male college students were used as test samples. Each disguised voice was compared with all normal voices in the database of 2000 speakers to make speaker identification and speaker verification. The result of speaker recognition was presented and the effect of voice disguise on the FASRS was evaluated.

Related Organizations

Criminal Investigation Police University of China
China (People's Republic of)

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	13
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average