A Small vocabulary automatic speech profanity suppression system using Hybrid Hidden Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework
Date of Publication
2010
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Electronics and Communications Engineering
College
Gokongwei College of Engineering
Department/Unit
Electronics and Communications Engineering
Thesis Adviser
Leonard U. Ambata
Defense Panel Chair
Enrique M. Manzano
Defense Panel Member
Jose Antonio M. Catalan
Noriel C. Mallari
Abstract/Summary
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 (CLSU). 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 feed-forward neural network using Multi-Layer Perception (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 female) Filipino speakers.
Abstract Format
html
Language
English
Format
Accession Number
TU15876
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
xvi, 357, 16 leaves : col. ill.; 28 cm.
Keywords
Automatic speech recognition; Speech processing systems
Recommended Citation
Ablaza, F. I., Danganan, T. D., Javier, B. L., Manalang, K. S., & Montalvo, D. V. (2010). A Small vocabulary automatic speech profanity suppression system using Hybrid Hidden Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/14685
