An open-set speaker identification system using genetic learning classifier system

Park, WonKyung; Oh,; Blowers, Misty; Wolf, Matt B.

doi:10.1145/1143997.1144259

Cited by 3 publications

(2 citation statements)

References 4 publications

(7 reference statements)

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“…On the other hand, spectral level has been extensively used in speaker recognition systems for feature extraction. Typical methods in this spectral level are as follows: Short-time spectrum (no matter if we use the exact representation or its approximation by filter banks) (Xiang and Berger, 2003 ; Seddik et al, 2004 ; Burget et al, 2007 ), predictor coefficients [based on a linear model of speech production: (Park et al, 2006 )], formant frequencies and bandwidth [defined as the resonance frequencies of the vocal tract: (Fatima et al, 2004 )], or even the formant trajectories (Tanabian et al, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Improving Speaker Recognition by Biometric Voice Deconstruction

Mazaira-Fernández

Álvarez-Marquina

Gómez-Vilda

2015

Front. Bioeng. Biotechnol.

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Person identification, especially in critical environments, has always been a subject of great interest. However, it has gained a new dimension in a world threatened by a new kind of terrorism that uses social networks (e.g., YouTube) to broadcast its message. In this new scenario, classical identification methods (such as fingerprints or face recognition) have been forcedly replaced by alternative biometric characteristics such as voice, as sometimes this is the only feature available. The present study benefits from the advances achieved during last years in understanding and modeling voice production. The paper hypothesizes that a gender-dependent characterization of speakers combined with the use of a set of features derived from the components, resulting from the deconstruction of the voice into its glottal source and vocal tract estimates, will enhance recognition rates when compared to classical approaches. A general description about the main hypothesis and the methodology followed to extract the gender-dependent extended biometric parameters is given. Experimental validation is carried out both on a highly controlled acoustic condition database, and on a mobile phone network recorded under non-controlled acoustic conditions.

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Section: Introductionmentioning

confidence: 99%

Improving Speaker Recognition by Biometric Voice Deconstruction

Mazaira-Fernández

Álvarez-Marquina

Gómez-Vilda

2015

Front. Bioeng. Biotechnol.

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“…One limitation that existed in most speaker identification studies was that those studies were done on closed-set systems. open-set speaker recognition system using a Genetic Learning Classifier System (LCS) [10]. They also claim it to be the first open-set system study in speaker recognition.…”

Section: Genetic Algorithms In Speaker Recognitionmentioning

confidence: 99%

Speaker Recognition Using Machine Learning Techniques

Sharma¹

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Speaker recognition is a technique of identifying the person talking to a machine using the voice features and acoustics. It has multiple applications ranging in the fields of Human Computer Interaction (HCI), biometrics, security, and Internet of Things (IoT). With the advancements in technology, hardware is getting powerful and software is becoming smarter. Subsequently, the utilization of devices to interact effectively with humans and performing complex calculations is also increasing. This is where speaker recognition is important as it facilitates a seamless communication between humans and computers. Additionally, the field of security has seen a rise in biometrics. At present, multiple biometric techniques co-exist with each other, for instance, iris, fingerprint, voice, facial, and more. Voice is one metric which apart from being natural to the users, provides comparable and sometimes even higher levels of security when compared to some traditional biometric approaches. Hence, it is a widely accepted form of biometric technique and is constantly being studied by scientists for further improvements. This study aims to evaluate different pre-processing, feature extraction, and machine learning techniques on audios recorded in unconstrained and natural environments to determine which combination of these works well for speaker recognition and classification. Thus, the report presents several methods of audio preprocessing like trimming, split and merge, noise reduction, and vocal enhancements to enhance the audios obtained from real-world situations. Additionally, a text-independent approach is used in this research which makes the model flexible to multiple languages. Mel Frequency Cepstral Coefficients (MFCC) are extracted for each audio, along with their differentials and accelerations to evaluate machine learning classification techniques such as kNN, Support Vector Machines, and Random Forest Classifiers. Lastly, the approaches are evaluated against existing research to study which techniques performs well on these sets of audio recordings.

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Graph bandit for diverse user coverage in online recommendation

Rahman

2017

Appl Intell

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An open-set speaker identification system using genetic learning classifier system

Cited by 3 publications

References 4 publications

Improving Speaker Recognition by Biometric Voice Deconstruction

Improving Speaker Recognition by Biometric Voice Deconstruction

Speaker Recognition Using Machine Learning Techniques

Graph bandit for diverse user coverage in online recommendation

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