Computer-Based Fluency Evaluation of English Speaking Tests for Koreans

Abstract

In this paper, we propose an automatic fluency evaluation algorithm for English speaking tests. In the proposed algorithm, acoustic features are extracted from an input spoken utterance and then fluency score is computed by using support vector regression (SVR). We estimate the parameters of feature modeling and SVR using the speech signals and the corresponding scores by human raters. From the correlation analysis results, it is shown that speech rate, articulation rate, and mean length of runs are best for fluency evaluation. Experimental results show that the correlation between the human score and the SVR score is 0.87 for 3 speaking tests, which suggests the possibility of the proposed algorithm as a secondary fluency evaluation tool. 1) 적은 묵음(pause)으로 구체적으로 말할 수 있는 능력 2) 응집력 있고 논리적으로 말하는 능력3) 다양한 문맥과 상황에서 적절히 말할 수 있는 능력

Fluency Scoring of English Speaking Tests for Nonnative Speakers Using a Native English Phone Recognizer

“…In our experiments, the effect of the speech recognizer on fluency scoring is compared with a phone aligner. For this purpose, we trained the acoustic model for the phone aligner with transcribed text [10]. When a transcribed text is available, the phone aligner produces the best results for phone segmentation.…”

“…The pauses here do not consider the ones between sentences but the ones inside the sentences. To avoid the problem due to hard clipping on pause duration and obtain the SUPR in a continuous value, a sigmoid function was applied to the duration of silent segments [10]. The lenUP is related to the length of unfilled pauses, and calculated as the total duration of unfilled pauses divided by the number of unfilled pauses [3].…”

“…As in the previous paper [10], we selected SVR as a predictor for fluency score because SVR is known to achieve good generalization performance by virtue of a nonlinear prediction function [18]- [21]. As shown in <Figure 3>, the basic goal of SVR is to find a function f (x) that has at most ɛ deviation from the actually obtained targets [21].…”

“…Jang and Kwon recently proposed a method for fluency and pronunciation evaluation using an aligner in case when the transcribed text is given [10]. But, it could not evaluate the fluency of free-talking utterances if the transcribed text is not available.…”

“…Type and time limit for each task in a test set different from the previous paper[10] because the scores were computed only for the fluency category.…”