Recognition of human speech phonemes using a novel fuzzy approach

Authors:
Ramin Halavati;Saeed Bagheri Shouraki;Saman Harati Zadeh
Affiliations:
Artificial Intelligence Lab 308, Computer Engineering Department, Sharif University of Technology, Tehran, Iran;Artificial Intelligence Lab 308, Computer Engineering Department, Sharif University of Technology, Tehran, Iran;Artificial Intelligence Lab 308, Computer Engineering Department, Sharif University of Technology, Tehran, Iran
Venue:
Applied Soft Computing
Year:
2007

Citing 5
Cited 2

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Fast and accurate acoustic modelling with semi-continuous HMMs

Speech Communication
Phoneme-based speech recognition via fuzzy neural networks modeling and learning

Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
Hybrid System for Robust Recognition of Noisy Speech Based on Evolving Fuzzy Neural Networks and Adaptive Filtering

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Phoneme recognition using ICA-based feature extraction and transformation

Signal Processing

Genetic learning of vocal tract area functions for articulatory synthesis of Spanish vowels

Applied Soft Computing
Automatic recognition of oral vowels in tone language: Experiments with fuzzy logic and neural network models

Applied Soft Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Recognition of human speech has long been a hot topic among artificial intelligence and signal processing researches. Most of current policies for this subject are based on extraction of precise features of voice signal and trying to make most out of them by heavy computations. But this focus on signal details has resulted in too much sensitivity to noise and as a result, the necessity of complex noise detection and removal algorithms, which composes a trade-off between fast or noise robust recognition. This paper presents a novel approach to speech recognition using fuzzy modeling and decision making that ignores noise instead of its detection and removal. To do so, the speech spectrogram is converted into a fuzzy linguistic description and this description is used instead of precise acoustic features. During the training period, a genetic algorithm finds appropriate definitions for phonemes, and when these definitions are ready, a simple novel operator consisting of low cost functions such as Max, Min, and Average makes the recognition. The approach is tested on a standard speech database and is compared with Hidden Markov model recognition system with MFCC features as a widely used speech recognition approach.