Automated rating of recorded classroom presentations using speech analysis in kazakh
This work addresses the need for automated assessment of presentation skills in Kazakh, which is incremental as it applies existing speech analysis methods to a new language.
The paper tackled the problem of automatically rating classroom presentations in Kazakh by analyzing speech features like intonation, establishing a threshold of 0.16 for distinguishing monotone from dynamic speech with an error rate of 19%.
Effective presentation skills can help to succeed in business, career and academy. This paper presents the design of speech assessment during the oral presentation and the algorithm for speech evaluation based on criteria of optimal intonation. As the pace of the speech and its optimal intonation varies from language to language, developing an automatic identification of language during the presentation is required. Proposed algorithm was tested with presentations delivered in Kazakh language. For testing purposes the features of Kazakh phonemes were extracted using MFCC and PLP methods and created a Hidden Markov Model (HMM) [5], [5] of Kazakh phonemes. Kazakh vowel formants were defined and the correlation between the deviation rate in fundamental frequency and the liveliness of the speech to evaluate intonation of the presentation was analyzed. It was established that the threshold value between monotone and dynamic speech is 0.16 and the error for intonation evaluation is 19%.