A small vocabulary automatic Filipino speech profanity suppression system using hybrid hidden Markov model/artificial neural network (HMM/ANN) keyword spotting framework

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2014 - 7th HNICEM 2014 Joint with 6th International Symposium on Computational Intelligence and Intelligent Informatics, co-located with 10th ERDT Conference

Publication Date

1-1-2014

Abstract

This paper describes an implementation of speech recognition that recognizes and suppresses ten (10) defined profane and vulgar Filipino words. The adapted speech recognition architecture was that of the Oregon Graduate Institute's (OGI) Center for Spoken Language and Learning (CSLU). It utilizes a hybrid Hidden Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. The feature extraction method used was Mel-Frequency Cepstral Coefficients (MFCC). The ANN is a 3-layer feedforward neural network using Multi-Layer Perceptron (MLP). In recognizing the words, an HMM decoder was used which implemented the Viterbi Beam Search Algorithm. Whenever a profane word was recognized, it would be replaced with a constant frequency tone. The training and testing data (recordings) were gathered from 30 random (15 male and 15 female) Filipino speakers. © 2014 IEEE.

html

Digitial Object Identifier (DOI)

10.1109/HNICEM.2014.7016183

Disciplines

Electrical and Computer Engineering

Keywords

Automatic speech recognition; Hidden Markov models; Neural networks (Computer science)

Upload File

wf_yes

This document is currently not available here.

Share

COinS