Digital Signal Processing

Anjali Diwan ¹, Bhargav Ravat ²

Affiliations
1 MBICT, Vallabh Vidyanagar, IN
2 CMC Limited, Hyderabad, IN

Abstract

Since even before the time of Alexander Graham Bell's revolutionary invention, engineers and scientists have studied the phenomenon of speech communication with an eye on creating more efficient and effective systems of human-to-human and human-to-machine communication digital signal processing (DSP), assumed a central role in speech studies. The first step is the extraction of feature vectors based on MFCC. The second is the classification of feature vectors using Vector quantization. The extracted acoustic parameters from the voice signals are used as an input for the MFCC. The main advantage of this method is less computation time and possibility of real-time system development. This paper introduces the design and implementation of the system for recognizing pathological and normal voice. In this ASR system we have used Vector quantization Algorithm.

Keywords

Mel Frequency Cepstral Coefficient (MFCC), Acoustic Parameters, Speech Processing, Vector Quantization, ASR, Hamming Window, Feature Extraction.

Full Text

     

Bhargav Ravat ¹, Arani Shah ², Arjun Bambhaniya ³, Anjali Diwan ⁴

Affiliations
1 VLSI Technology, IN
2 Birls Vishwakarma Mahavidyalaya, Gujarat Technological University, IN
3 M.S.U. of Vadodara, IN
4 Shri Shankaracharya Institute of Engineering and Technology , Bhilai, IN

Abstract

Speech, iris, face, finger print are the fundamental parameters that can help in designing a biometric authentication system. These kinds of systems are helpful in recognizing the identity of the authenticated person. The voice is a signal of infinite information. Speech based biometric authentication system is the most successful one due to simple nature of acquiring the voice and uniqueness associated with it. In this paper Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. The mel frequency cepstral coefficient (MFCC) is one of the most important features required among various kinds of speech applications. Fundamental motto is to design a safety box which is being operated by the voice through MATLAB - MFCC and GUI. The system developed is able to recognize specific user by extracting various characteristics of speech signals. Tested on various sample speech which belongs to male and female of various age group. The level of accuracy obtained from MFCC is much higher than of the other concepts like Dynamic Time Wrapping and Perceptual Linear Prediction. Another advantage is its ease of usage which is being obtained through its GUI concept.

Keywords

Mel Frequency Cepstral Coefficient (MFCC), GUI, Speech Processing, FFT, DCT, Hamming Window, Feature Extraction.

Full Text