Automated rating of recorded classroom presentations using speech analysis in kazakh

TL;DR

This paper develops an automated speech evaluation system for Kazakh classroom presentations, focusing on intonation and speech dynamics, utilizing phoneme features and Hidden Markov Models to assess presentation quality.

Contribution

It introduces a novel algorithm for speech assessment tailored to Kazakh language presentations, incorporating phoneme feature extraction and intonation analysis.

Findings

Threshold for monotone vs. dynamic speech is 0.16.

Intonation evaluation error rate is 19%.

Speech liveliness correlates with deviation in fundamental frequency.

Abstract

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%.