A Mandarin e‐learning system based on speech recognition and evaluation

Abstract

This paper establishes a speaker-independent pronunciation recognition and assessment system under the background of a Mandarin e-learning system framework. The recognition part is based on Hidden Markov Models (HMM) and improved in the aspect of acoustic and tone model. Making use of the recognition and detection results and corresponding parametric scorings, the machine scoring is performed to evaluate the quality of pronunciation its correlation with expert score are discussed. Through integrating recognition and assessment system with Web 2.0, we ultimately establish an open, shared and growing-up virtual Mandarin learning community, where all of learners, viewers, professors, without any restrictions on time and places, can express their perspective about how to advance Mandarin learning freely. ß

Effectiveness of Cognitive Academic Language Learning Approach Model in Chinese Language Learning

“…Mandarin is a tonal language, it is important to have accuracy in pronouncing tones in real communication (Ming & Bai, 2011). Problems occurring with the pronunciation of Mandarin often occur for beginner learners.…”

Investigating motivational dimensions for e-learning: A case study of dental students

“…There are researches on e-learning reported in various countries such as in Thailand [1], Singapore, Malaysia, Indonesia, Dubai [2], Jordan [3], Japan [4] and China [5]. Furthermore, the scope of research had transcended to incorporate values such as; (i) trust [6] (ii) motivation [7] [8], (iii) affective [9] and (iv) empathy [10].…”