Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features /
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| Main Author: | |
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| Format: | Thesis Software eBook |
| Language: | English |
| Published: |
Perlis, Malaysia
School of Mechatronic Engineering
2012.
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| Physical Description: | 1 CD-ROM 12 cm |
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| Bibliography: | 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)). |