Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features
Classroom speech intelligibility is a measure of how well a speech or word is understood in the classroom. It is a measure of the speech quality in the classroom. Numbers of methods have been proposed by various researchers to improve the speech intelligibility. However, the proposed methods are...
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my-unimap-313062014-01-19T08:41:36Z Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features Mohammad Ridhwan, Tamjis Classroom speech intelligibility is a measure of how well a speech or word is understood in the classroom. It is a measure of the speech quality in the classroom. Numbers of methods have been proposed by various researchers to improve the speech intelligibility. However, the proposed methods are effective only in the design stage of the classroom, as implementation in the ‘old’ classroom is costly and time consuming. Thus, speech amplification is implemented to tackle such problems. There are methods suggested by audio expert on how to properly setup the system in the classroom, in order to maximize the speech intelligibility. However, the methods are rather complicated and time consuming. So, as an alternative, this research has proposed an audio-feature based speech intelligibility prediction system. 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)). To achieve the goal, this research has collected data samples which comprises of speech recordings in the speech amplified classrooms, as well as the physical properties. The measurement was done in eight different classrooms in UniMAP, and the measurement protocol was derived from the previous researches and acoustic standards. The data collected were then analyzed using statistical approach, such as descriptive analysis and ANOVA. The data were then pre-processed to assist the later feature extraction process. The preprocessed signals were then undergone feature extraction process to extract the audio features. In this research, five types of audio features have been selected, and each feature is then combined with the classroom’s physical feature data as inputs of the experimented classifiers. As a result, it was found that audio feature PO yield the best accuracy, regardless the type of classifiers when compared to the other features. At the end, the interface system for the audio feature-based classroom speech intelligibility prediction system is developed. Moreover, a database of classroom speech intelligibility measurement using single microphone was compiled. Universiti Malaysia Perlis (UniMAP) 2012 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/31306 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31306/1/Page%201-24.pdf 6df246584ae54161875f7585905365ac http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31306/2/Full%20text.pdf 665dd1f8ce5db049a384876ddef0d510 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31306/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Speech intelligibility Classroom speech Prediction Speech quality measure Speech amplification School of Mechatronic Engineering |
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Universiti Malaysia Perlis |
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UniMAP Institutional Repository |
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English |
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Speech intelligibility Classroom speech Prediction Speech quality measure Speech amplification |
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Speech intelligibility Classroom speech Prediction Speech quality measure Speech amplification Mohammad Ridhwan, Tamjis Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features |
| description |
Classroom speech intelligibility is a measure of how well a speech or word is
understood in the classroom. It is a measure of the speech quality in the classroom.
Numbers of methods have been proposed by various researchers to improve the
speech intelligibility. However, the proposed methods are effective only in the design
stage of the classroom, as implementation in the ‘old’ classroom is costly and time
consuming. Thus, speech amplification is implemented to tackle such problems. There
are methods suggested by audio expert on how to properly setup the system in the
classroom, in order to maximize the speech intelligibility. However, the methods are
rather complicated and time consuming. So, as an alternative, this research has
proposed an audio-feature based speech intelligibility prediction system. 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)). To achieve the goal, this research
has collected data samples which comprises of speech recordings in the speech
amplified classrooms, as well as the physical properties. The measurement was done
in eight different classrooms in UniMAP, and the measurement protocol was derived
from the previous researches and acoustic standards. The data collected were then
analyzed using statistical approach, such as descriptive analysis and ANOVA. The
data were then pre-processed to assist the later feature extraction process. The preprocessed
signals were then undergone feature extraction process to extract the audio
features. In this research, five types of audio features have been selected, and each
feature is then combined with the classroom’s physical feature data as inputs of the
experimented classifiers. As a result, it was found that audio feature PO yield the best
accuracy, regardless the type of classifiers when compared to the other features. At the
end, the interface system for the audio feature-based classroom speech intelligibility
prediction system is developed. Moreover, a database of classroom speech
intelligibility measurement using single microphone was compiled. |
| format |
Thesis |
| author |
Mohammad Ridhwan, Tamjis |
| author_facet |
Mohammad Ridhwan, Tamjis |
| author_sort |
Mohammad Ridhwan, Tamjis |
| title |
Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features |
| title_short |
Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features |
| title_full |
Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features |
| title_fullStr |
Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features |
| title_full_unstemmed |
Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features |
| title_sort |
classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features |
| granting_institution |
Universiti Malaysia Perlis (UniMAP) |
| granting_department |
School of Mechatronic Engineering |
| url |
http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31306/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31306/2/Full%20text.pdf |
| _version_ |
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