A class of neuro-computational methods for assamese fricative classification

• Autorzy: Patgiri C. , Sarma M. , Sarma K. K.

Identyfikatory

DOI

10.1515/jaiscr-2015-0019

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

neuro-computational classifier; fricative phonemes; Assamese language; recurrent neural network; RNN; neuro fuzzy classifier; linear prediction cepstral coefficients; LPCC; self-organizing map; SOM; adaptive neuro-fuzzy inference system; ANFIS

PL

klasyfikator neuronowy; RNN; klasyfikator neuronowo rozmyty; sieć Kohonena; SOM; ANFIS

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2015
• Tom: Vol. 5, No. 1
• Strony: 59--70
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

Patgiri C.

• Department of Applied Electronics and Instrumentation, GIMT al., Guwahati-781017, Assam, India

autor

Sarma M.

• Department of Electronics and Communication Engineering, Gauhati University al., Guwahati- 781014, Assam, India

autor

Sarma K. K.

• Department of Electronics and Communication Technology, Gauhati University al. Guwahati- 781014, Assam, India

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