A prediction model of speech transmission index based on reverberation time in non-native linguistic context

High speech intelligibility is an essential requirement for classrooms, especially in relation to non-native students. Speech transmission index (STI) was proved as the most relevant acoustic parameter to assess speech intelligibility. In this paper, twenty-seven classrooms for non-native teaching purposes were selected for investigation. Physical acoustic measurements were conducted in these classrooms and numerical simulation verification was determined by ODEON version 16. The relationships between STI values and RT values were fitted based on non-linear curve fitting regression models. In this paper, three primary forms of non-linear curve fitting regression models were employed for predicting curves. A logarithmic function was selected as the basic regression equation to describe the effects of RT values on STI values. The results showed that STI values increase with the decrease of RT values for all age groups. From the verified results, it was possible to propose the predictive equation that presents the best accuracy in predicting the experimental data for non-native teaching purposes. The impact of different age groups and linguistic environment on STI were discussed. The prediction model is expected to estimate STI values by using RT values during the early design stage in a non-native linguistic context.