Version 1
: Received: 30 October 2018 / Approved: 31 October 2018 / Online: 31 October 2018 (08:14:15 CET)
How to cite:
MIAN QAISAR, S. An Efficient Isolated Speech Recognition Based on the Adaptive Rate Processing and Analysis. Preprints2018, 2018100739. https://doi.org/10.20944/preprints201810.0739.v1
MIAN QAISAR, S. An Efficient Isolated Speech Recognition Based on the Adaptive Rate Processing and Analysis. Preprints 2018, 2018100739. https://doi.org/10.20944/preprints201810.0739.v1
MIAN QAISAR, S. An Efficient Isolated Speech Recognition Based on the Adaptive Rate Processing and Analysis. Preprints2018, 2018100739. https://doi.org/10.20944/preprints201810.0739.v1
APA Style
MIAN QAISAR, S. (2018). An Efficient Isolated Speech Recognition Based on the Adaptive Rate Processing and Analysis. Preprints. https://doi.org/10.20944/preprints201810.0739.v1
Chicago/Turabian Style
MIAN QAISAR, S. 2018 "An Efficient Isolated Speech Recognition Based on the Adaptive Rate Processing and Analysis" Preprints. https://doi.org/10.20944/preprints201810.0739.v1
Abstract
This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.
Keywords
Event-Driven Processing, Speech recognition, Adaptive Resolution Analysis, Features extraction, Dynamic Time Warping, Classification
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
