134 research outputs found
Fast Speaker Verification on Mobile Phone data using Boosted Slice Classifiers
In this work, we investigate a novel computationally efficient speaker verification (SV) system involving boosted ensembles of simple threshold-based classifiers. The system is based on a novel set of features called âslice featuresâ. Both the system and the features were inspired by the recent success of pixel comparison-based ensemble approaches in the computer vision domain. The performance of the proposed system was evaluated through speaker verification experiments on the MOBIO corpus containing mobile phone speech, according to a challenging protocol. The system was found to perform reasonably well, compared to multiple state-of-the-art SV systems, with the benefit of significantly lower computational complexity. Its dual characteristics of good performance and computational efficiency could be important factors in the context of SV system implementation on portable devices like mobile phones
A Fast Parts-based Approach to Speaker Verification using Boosted Slice Classifiers
Speaker verification on portable devices like smartphones is gradually becoming popular. In this context, two issues need to be considered: 1) such devices have relatively limited computation resources, and 2) they are liable to be used everywhere, possibly in very noisy, uncontrolled environments. This work aims to address both these issues by proposing a computationally efficient yet robust speaker verification system. This novel parts-based system draws inspiration from face and object detection systems in the computer vision domain. The system involves boosted ensembles of simple threshold-based classifiers. It uses a novel set of features extracted from speech spectra, called âslice featuresâ. The performance of the proposed system was evaluated through extensive studies involving a wide range of experimental conditions using the TIMIT, HTIMIT and MOBIO corpus, against standard cepstral features and Gaussian Mixture Model-based speaker verification systems
Computer Graphics and Video Features for Speaker Recognition
Tato prĂĄce popisuje netradiÄnĂ metodu rozpoznĂĄvĂĄnĂ ĆeÄnĂka pomocĂ pĆĂznakĆŻ a alogoritmĆŻ pouĆŸĂvanĂœch pĆevĂĄĆŸnÄ v poÄĂtaÄovĂ©m vidÄnĂ. V Ășvodu jsou shrnuty potĆebnĂ© teoretickĂ© znalosti z oblasti poÄĂtaÄovĂ©ho rozpoznĂĄvĂĄnĂ. Jako aplikace grafickĂœch pĆĂznakĆŻ v rozpoznĂĄvĂĄnĂ ĆeÄnĂka jsou detailnÄji popsĂĄny jiĆŸ znĂĄmĂ© BBF pĆĂznaky. Tyto jsou vyhodnoceny nad standardnĂmi ĆeÄovĂœmi databĂĄzemi TIMIT a NIST SRE 2010. ExperimentĂĄlnĂ vĂœsledky jsou shrnuty a porovnĂĄny se standardnĂmi metodami. V zĂĄvÄru jsou jsou navrĆŸeny moĆŸnĂ© smÄry budoucĂ prĂĄce.We describe a non-traditional method for speaker recognition that uses features and algorithms used mainly for computer vision. Important theoretical knowledge of computer recognition is summarized first. The Boosted Binary Features are described and explored as an already proposed method, that has roots in computer vision. This method is evaluated on standard speaker recognition databases TIMIT and NIST SRE 2010. Experimental results are given and compared to standard methods. Possible directions for future work are proposed at the end.
Biometrics
Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about
Acoustic Approaches to Gender and Accent Identification
There has been considerable research on the problems of speaker and language recognition
from samples of speech. A less researched problem is that of accent recognition. Although this
is a similar problem to language identification, diïżœerent accents of a language exhibit more
fine-grained diïżœerences between classes than languages. This presents a tougher problem
for traditional classification techniques. In this thesis, we propose and evaluate a number of
techniques for gender and accent classification. These techniques are novel modifications and
extensions to state of the art algorithms, and they result in enhanced performance on gender
and accent recognition.
The first part of the thesis focuses on the problem of gender identification, and presents a
technique that gives improved performance in situations where training and test conditions are
mismatched.
The bulk of this thesis is concerned with the application of the i-Vector technique to accent
identification, which is the most successful approach to acoustic classification to have emerged
in recent years. We show that it is possible to achieve high accuracy accent identification without
reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis
describes various stages in the development of i-Vector based accent classification that improve
the standard approaches usually applied for speaker or language identification, which are
insuïżœcient. We demonstrate that very good accent identification performance is possible with
acoustic methods by considering diïżœerent i-Vector projections, frontend parameters, i-Vector
configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can
obtain from the same data.
We claim to have achieved the best accent identification performance on the test corpus
for acoustic methods, with up to 90% identification rate. This performance is even better than
previously reported acoustic-phonotactic based systems on the same corpus, and is very close
to performance obtained via transcription based accent identification. Finally, we demonstrate
that the utilization of our techniques for speech recognition purposes leads to considerably
lower word error rates.
Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian
Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British
English, Prosody, Speech Recognition
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