Recognition of human speech phonemes using a novel fuzzy approach

Halavati, Ramin; Shouraki, Saeed Bagheri; Zadeh, Saman Harati

doi:10.1016/j.asoc.2006.02.007

Cited by 20 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, however, the use of soft-computing techniques in speech processing in general [17,27,48,56] and in speech recognition in particular [3,4,11,26,57] is growing. Ray and Ghoshal [26] applied neuro-fuzzy model in the development of an ASR for vowels of three Indian languages, namely: Telugu, Assamese and Bengali.…”

Section: Related Workmentioning

confidence: 99%

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

íkínbí

Odéjobí

2011

Applied Soft Computing

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

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

íkínbí

Odéjobí

2011

Applied Soft Computing

View full text Add to dashboard Cite

“…MFCC's are based on the known variation of the human ear's critical bandwidths with frequency; filters spaced linearly at low frequencies and logarithmically at high frequencies have been used to capture the phonetically important characteristics of speech. This is expressed in the Mel-frequency scale, which is linear frequency spacing below 1000 Hz and a logarithmic spacing above 1000 Hz [3]. MFCC is the best known and most popular so we decided to use MFCC in our project.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…M. Benzeghiba, R. De Mori, [2] proposed an Automatic speech recognition and speech variability. Ramin Halavati, Saeed Bagheri Shouraki, [3] proposed a Recognition of human speech phonemes using a novel fuzzy approach.…”

Section: Introductionmentioning

confidence: 99%

Development an Automatic Translate Voice to Sign Language Animation Based-on MFCC and Vector Quantization Method

Kasaei¹,

Kasaei²

2014

IJCEE

View full text Add to dashboard Cite

Abstract-In this paper, we propose design and initial implementation of a robust system which can automatically translates voice into text and text to sign language animations. Sign Language Translation Systems could significantly improve deaf lives especially in communications, exchange of information and employment of machine for translation conversations from one language to another has. Therefore, considering these points, it seems necessary to study the speech recognition. Usually, the voice recognition algorithms address three major challenges. The first is extracting feature form speech and the second is when limited sound gallery are available for recognition, and the final challenge is to improve speaker dependent to speaker independent voice recognition. Extracting feature form speech is an important stage in our method. Different procedures are available for extracting feature form speech. One of the commonest of which used in speech recognition systems is Mel-Frequency Cepstral Coefficients (MFCCs). The algorithm starts with preprocessing and signal conditioning. Next extracting feature form speech using Cepstral coefficients will be done. Then the result of this process sends to segmentation part. Finally recognition part recognizes the words and then converting word recognized to facial animation. The project is still in progress and some new interesting methods are described in the current report.

show abstract

“…The ANFIS employs a hybrid-learning algorithm, which is a combination of the recursive least-squares (RLS) method and the back propagation gradient descent method for training Sugeno-type FIS membership function parameters to replicate the given training data set [24]. Use of NFS approach for speech recognition has recently been reported in contribution like [11], [12], [13], [14] and [15].…”

Section: Introductionmentioning

confidence: 99%

A Class of Neuro-Computational Methods for Assamese Fricative Classification

Patgiri¹,

Sarma

2015

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.

show abstract

Recognition of human speech phonemes using a novel fuzzy approach

Cited by 20 publications

References 5 publications

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

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

Development an Automatic Translate Voice to Sign Language Animation Based-on MFCC and Vector Quantization Method

A Class of Neuro-Computational Methods for Assamese Fricative Classification

Contact Info

Product

Resources

About