Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features /
The goal of this research is to develop an intelligent speech intelligibility prediction system by combining audio-features (spectral rolloff (SR), spectral centroid (SC), power (PO), zero-crossings rate (ZCR), and short time energy (STE)) and classifiers (feed forward neural network (FFNN), Elman n...
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| Main Author: | |
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| Format: | Thesis Book |
| Language: | English |
| Published: |
Perlis, Malaysia
School of Mechatronic Engineering
2012.
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| Summary: | The goal of this research is to develop an intelligent speech intelligibility prediction system by combining audio-features (spectral rolloff (SR), spectral centroid (SC), power (PO), zero-crossings rate (ZCR), and short time energy (STE)) and classifiers (feed forward neural network (FFNN), Elman network (ENN)). |
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| Physical Description: | 125 pages illustrations 30 cm |
| Bibliography: | Includes bibliographical references (pages 112-118). |