This paper presents robust feature extraction techniques for isolated word recognition under noisy conditions. The proposed hybrid feature extraction techniques are Bark Frequency Cepstral Coefficients (BFCC) and Weighted Average Mel-Frequency Cepstral Coefficient (WMFCC). Both methods are tested in various noisy environments using a single Gaussian Hidden Markov Model (HMM) based isolated digit recognition system. The results clearly indicates that WMFCC performed well compared to Mel-Frequency Cepstral Coefficient (MFCC) in noisy environment using NOISEX-92 database. © 2014 IEEE.

John Sahaya Rani Alex

Electronics

School of Electronics Engineering

Chennai Campus

Mukhedkar A.S

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Robust feature extraction methods for speech recognition in noisy environments

2014 First International Conference on Networks & Soft Computing (ICNSC2014)

Robust feature extraction techniques play an important role in speaker recognition system. Four speech feature extraction techniques such as Mel-Frequency Cepstral Coefficient (MFCC), Linear Prediction Cepstrum Coefficient (LPCC), Perceptual Linear Predictive (PLP), and Wavelet Cepstral Coefficient (WCC) techniques are analyzed for extracting speaker-specific information. The design of WCC method is done for this work. Hidden Markov Model (HMM) is used to model each speaker from the speaker-specific speech features. The conventional Person Identification System (PIS) is normally employed in an environment where the background noise is unavoidable. To simulate such environment, an additive white Gaussian noise of different SNRs is added with a studio quality speech data. Evaluation of PIS is performed using the Hidden Markov Toolkit (HTK). Multiple experiments are performed. Acoustic modeling of speaker and evaluation is done for clean and noisy environment. The experiment results indicate that 100% accuracy for text-independent PIS in a clean environment. Furthermore, it is observed that MFCC is proven to be better noise robust than PLP and LPC. It is also noted that dynamic features such as delta and acceleration features are combined with static features improve the performance of the PIS in noisy environment. © Springer Nature Singapore Pte Ltd. 2018.

Lecture Notes in Electrical Engineering Computational Signal Processing and Analysis

Evaluation of Cepstral Features of Speech for Person Identification System Under Noisy Environment

Second International Conference on Digital Image Processing

Comparative wavelet, PLP, and LPC speech recognition techniques on the Hindi speech digits database

Journal of Computer Science

Comparison of Speech Features on the Speech Recognition Task

Proceedings of the IEEE

A tutorial on hidden Markov models and selected applications in speech recognition

Journal	Data powered by Typeset2014 First International Conference on Networks & Soft Computing (ICNSC2014)
Publisher	Data powered by TypesetIEEE
Open Access	0