Emerging features for speaker recognition

Cepstral features and their derivatives have been successfully used in speaker recognitions tasks over the past two decades. Recently new features are emerging that make use of information that is not contained in cepstral features or their derivative, namely the phase spectra and instantaneous freq...

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Bibliographic Details
Published in2007 6th International Conference on Information, Communications and Signal Processing pp. 1 - 7
Main Author Ambikairajah, E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2007
Subjects
AM-FM
Australia
Band pass filters
Cepstral analysis
cepstral features
Communications technology
Delay estimation
empirical mode decomposition
Fourier transforms
Frequency modulation
group delay
modulation features
phase spectra
Speaker recognition
Speech analysis
Speech synthesis
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ISBN1424409829
9781424409822
DOI10.1109/ICICS.2007.4449889

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Summary:Cepstral features and their derivatives have been successfully used in speaker recognitions tasks over the past two decades. Recently new features are emerging that make use of information that is not contained in cepstral features or their derivative, namely the phase spectra and instantaneous frequency. There are numerous approaches to capture instantaneous amplitude and frequency variations in the signal, resulting in an amplitude and frequency modulation (AM-FM) feature set. While the addition of modulation features has been shown to improve speech recognition systems based on cepstral features, their use in speaker recognition is new and has not been fully explored. This paper outlines some of these emerging features and their extraction methods for use in speaker recognition tasks. The paper also talks about the recently proposed Empirical Mode Decomposition (EMD) as an alternative method of extracting instantaneous frequency.
ISBN:1424409829
9781424409822
DOI:10.1109/ICICS.2007.4449889