Automatic Voice and Speech Recognition System for the German Language with Deep Learning Methods

Year 2016, Special Issue (2016), 399 - 403, 01.12.2016

Cigdem Bakir

https://doi.org/10.18100/ijamec.280579

Abstract

In our age, technological
developments are accompanied by certain problems associated with them. Security
takes the first place amongst such kind of problems. In particular, such
biometric systems as authentication constitute the significant fraction of the
security matters. This is because sound recordings having connection with the
various crimes are required to be analyzed for forensic purposes.
Authentication systems necessitate transmission, design and classification of
biometric data in a secure manner. In this study, analysis of German language
employed in the economy, industry and trade in a wide spread manner, has been
performed. In the same vein, the aim was to actualize automatic voice and
speech recognition system using Mel Frequency Cepstral Coefficients (MFCC),
MelFrequency Discrete Wavelet Coefficients (MFDWC) and Linear. Prediction
Cepstral Coefficient (LPCC) taking German sound forms and properties into
consideration. Approximately 2658 German voice samples of words and clauses
with differing lengths have been collected from 50 males and 50 females.
Features of these voice samples have been obtained using wavelet transform.
Feature vectors of the voice samples obtained have been trained with such
methods as Boltzmann Machines and Deep Belief Networks. In the test phase,
owner of a given voice sample has been identified taking the trained voice
samples into consideration. Results and performances of the algorithms employed
in the study for classification have been also demonstrated in a comparative
manner.

Keywords

Boltzmann Machines

References

• [1] Douglas, Reynold , Walter, Andrews and Joseph, Campbell etc., “The SuperSID Project: Exploiting High-Level Information for High-Accuracy Speaker Recognition”, In.Proc. ICASSP, Hong Kong, p.784-787, 2003.
• [2] Douglas, Reynolds , Thomas, Quatieri and Robert, Dunn, “Speaker Vrification using Adapted Gaussian Mixture Models”, Digital Signal Processing 10, p.19-41, 2000.
• [3] Edmondo, Trentin and Marko, Gori, “A survey of hybrid ANN/HMM models for automatic speech recognition”, Elsevier Neurocomputing 37, p.91-126, 2001.
• [4] Keiichi, Tokuda , Heiga, Zen and Alan, Black, “An HMM-Based Speech Synthesis System Applied to English”, Proc.of 2002 IEEE SSW, p.227-230, 2012.
• [5] Lihang, Li, Dongqing, Chen and Sarang, Lakare etc, “Image segmentation approach to extract colon lümen through colonic material taggng and hidden markov random field model for virtual colonoskopy”, Medical Imaging, 2002.
• [6] Lindasalwa, Muda and Mumtaj, Began, “Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques”, Journal Computing, vol.2, issue 3,p.138-143, ISBN 2151-9617, 2010.