BROWSE
Detail
Conference Paper
Deep-Learning Based Automatic Spontaneous Speech Assessment in a Data-Driven Approach for the 2017 SLaTE CALL Shared Challenge
Cited - time in 
Share 
Share
- Authors
- Yoo Rhee Oh, Hyung-Bae Jeon, Hwa Jeon Song, Byung Ok Kang, Yun-Kyung Lee, Jeon-Gue Park, Yun-Keun Lee
- Issue Date
- 2017-08
- Citation
- Speech and Language Technology in Education (SLaTE) Workshop 2017, pp.111-116
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper presents a deep-learning based assessment method of a spoken computer-assisted language learning (CALL) for a non-native child speaker, which is performed in a data-driven approach rather than in a rule-based approach. Especially, we focus on the spoken CALL assessment of the 2017 SLaTE challenge. To this end, the proposed method consists of four main steps: speech recognition, meaning feature extraction, grammar feature extraction, and deep-learning based assessment. At first, speech recognition is performed on an input speech using three automatic speech recognition (ASR) systems. Second, twenty-seven meaning features are extracted from the recognized texts via the three ASRs using language models (LMs), sentence-embedding models, and wordembedding models. Third, twenty-two grammar features are extracted from the recognized text via one ASR system using linear-order LMs and hierarchical-order LMs. Fourth, the extracted forty-nine features are fed into a full-connected deep neural network (DNN) based model for the classification of acceptance or rejection. Finally, an assessment is performed by comparing the probability of a output unit of the DNN-based classifier with a predefined threshold. For the experiments of a spoken CALL assessment, we use English spoken utterances by Swiss German teenagers. It is shown from the experiments that the D score is 4.37 for the spoken CALL assessment system employing the proposed method.
- KSP Keywords
- Acceptance or rejection, Assessment method, Assessment system, Data-driven approach, Deep neural network(DNN), Embedding model, Feature extractioN, Language model, Rule-based Approach, automatic speech recognition(ASR), computer assisted language learning