Text-independent speaker recognition for Ambient Intelligence applications by using Information Set Features
Contributo in Atti di convegno
Data di Pubblicazione:
2017
Citazione:
Text-independent speaker recognition for Ambient Intelligence applications by using Information Set Features / A. Anand, R. Donida Labati, M. Hanmandlu, V. Piuri, F. Scotti (... IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND VIRTUAL ENVIRONMENTS FOR MEASUREMENT SYSTEMS AND APPLICATIONS). - In: Computational Intelligence and Virtual Environments for Measurement Systems and Applications[s.l] : Institute of Electrical and Electronics Engineers (IEEE), 2017 Jul. - ISBN 9781509042531. - pp. 30-35 (( convegno CIVEMSA 2017 tenutosi a Annecy nel 2017 [10.1109/CIVEMSA.2017.7995297].
Abstract:
Biometric systems are enabling technologies for a wide set of applications in Ambient Intelligence (AmI) environments. In this context, speaker recognition techniques are of paramount importance due to their high user acceptance and low required cooperation. Typical applications of biometric recognition in AmI environments are identification techniques designed to recognize individuals in small datasets. Biometric recognition methods are frequently deployed on embedded hardware and therefore need to be optimized in terms of computational time as well as used memory. This paper presents a text-independent speaker recognition method particularly suitable for identification in AmI environments. The proposed method first computes the Mel Frequency Cepstral Coefficients (MFCC) and then creates Information Set Features (ISF) by applying a fuzzy logic approach. Finally, it estimates the user's identity by using a hierarchical classification technique based on computational intelligence. We evaluated the performance of the speaker recognition method using signals belonging to the NIST-2003 switchboard speaker database. The achieved results showed that the proposed method reduced the size of the template with respect to traditional approaches based on Gaussian Mixture Models (GMM) and achieved better identification accuracy.
Tipologia IRIS:
03 - Contributo in volume
Elenco autori:
A. Anand, R. Donida Labati, M. Hanmandlu, V. Piuri, F. Scotti
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Link al Full Text:
Titolo del libro:
Computational Intelligence and Virtual Environments for Measurement Systems and Applications