| dc.description.abstract | 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. | en_US |
