It is the purpose of grouping here is that each FFT value is multiplied against the corresponding filter gain and the result is summed.
Published in Chapter:
Voice-Based Speaker Identification and Verification
Keshav Sinha (Birla Institute of Technology, India), Rasha Subhi Hameed (College of Education for Pure Sciences, Diyala University, Iraq), Partha Paul (Sarala Birla University, India), and Karan Pratap Singh (Sarala Birla University, India)
Copyright: © 2021
|Pages: 29
DOI: 10.4018/978-1-7998-7258-0.ch016
Abstract
In recent years, the advancement in voice-based authentication leads in the field of numerous forensic voice authentication technology. For verification, the speech reference model is collected from various open-source clusters. In this chapter, the primary focus is on automatic speech recognition (ASR) technique which stores and retrieves the data and processes them in a scalable manner. There are the various conventional techniques for speech recognition such as BWT, SVD, and MFCC, but for automatic speech recognition, the efficiency of these conventional recognition techniques degrade. So, to overcome this problem, the authors propose a speech recognition system using E-SVD, D3-MFCC, and dynamic time wrapping (DTW). The speech signal captures its important qualities while discarding the unimportant and distracting features using D3-MFCC.