Voice recognition based on adaptive MFCC and deep learning

In this paper, we propose an enhanced voice recognition method using Adaptive MFCC and Deep Learning. To improve the voice recognition rate, it is important to extract the audio data from original signal. However the existing Algorithms which is used to remove the noise of particular band deteriorat...

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Bibliographic Details
Main Authors: Hyan-Soo Bae, Ho-Jin Lee, Suk-Gyu Lee
Format: Conference Proceeding
Language:English
Subjects:
Deep Learning
Discrete Fourier transforms
Filter
Filter banks
Mel frequency cepstral coefficient
MFCC
Noise
Robustness
Speech recognition
Voice recognition
Wiener filters
Online Access:Request full text
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Summary:In this paper, we propose an enhanced voice recognition method using Adaptive MFCC and Deep Learning. To improve the voice recognition rate, it is important to extract the audio data from original signal. However the existing Algorithms which is used to remove the noise of particular band deteriorate the audio signal. Differently from the existing MFCC, the proposed filter is built up compactly in the data density area to reduce data loss, and impose the weighted value to the data area. As a result, it prevents the data loss which results in improving the recognition rate. In addition the Deep Learning makes it possible to use the Voice recognition without DB. Therefore, it can be effectively used for electronic devices with small memory.
ISSN:2158-2297
DOI:10.1109/ICIEA.2016.7603830