Speaker Verification System using a Hierarchical Adaptive Network-based Fuzzy Inference Systems (HANFIS)

Abstract : We propose the use of a hierarchical adaptive network-based fuzzy inference system (HANFIS) for automated speaker verification of Persian speakers from their English pronunciation of words. The proposed method uses three classes of sound properties consisting of linear prediction coefficients (LPC), word time- length, intensity and pitch, as well as frequency properties from FFT analysis. Actual audio data is collected from fourteen Persian speakers who spoke English. False acceptance ratio and false rejection ratio as are evaluated for various HANFIS trained with different radius. Results indicate that vowel sounds can be a good indicator for more accurate speaker verification. Finally, the hierarchical architecture is shown to considerably improve performance than ANFIS.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [5 references]  Display  Hide  Download

https://hal.inria.fr/hal-01054589
Contributor : Hal Ifip <>
Submitted on : Thursday, August 7, 2014 - 3:23:26 PM
Last modification on : Friday, August 11, 2017 - 11:17:08 AM
Document(s) archivé(s) le : Wednesday, November 26, 2014 - 1:47:03 AM

File

232.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Zohreh Soozanchi-K., Mohammad-R. Akbarzadeh-T., Mahdi Yaghoobi, Saeed Rahati. Speaker Verification System using a Hierarchical Adaptive Network-based Fuzzy Inference Systems (HANFIS). Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. pp.233-237, ⟨10.1007/978-3-642-15286-3_23⟩. ⟨hal-01054589⟩

Share

Metrics

Record views

253

Files downloads

185