Speech signal consists of events in time and frequency, and therefore
its analysis with high-resolution time-frequency tools is often of
importance. Analytic filter bank provides a simple, fast, and flexible
method to construct time-frequency representations of signals. Its
parameters can be easily adapted to different situations from uniform
to any auditory frequency scale, or even to a focused resolution. Since
the Hilbert magnitude values of the channels are obtained at every
sample, it provides a practical tool for a high-resolution time-frequency
analysis.
The present study describes the basic theory of analytic filters
and tests their main properties. Applications of analytic filter bank
to different speech analysis tasks including pitch period estimation
and pitch synchronous analysis of formant frequencies and bandwidths
are demonstrated. In addition, a new feature vector called group delay
vector is introduced. It is shown that this representation provides
comparable, or even better results, than those obtained by spectral
magnitude feature vectors in the analysis and classification of vowels.
The implications of this observation are discussed also from the speech
perception point of view.
Cite as: Laine, U.K. (2017) Analytic Filter Bank for Speech Analysis, Feature Extraction and Perceptual Studies. Proc. Interspeech 2017, 449-453, doi: 10.21437/Interspeech.2017-1232
@inproceedings{laine17_interspeech,
author={Unto K. Laine},
title={{Analytic Filter Bank for Speech Analysis, Feature Extraction and Perceptual Studies}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={449--453},
doi={10.21437/Interspeech.2017-1232}
}