Automatic proficiency assessment of Korean speech read aloud by non‐natives using bidirectional LSTM‐based speech recognition

Abstract

This paper presents an automatic proficiency assessment method for a non‐native Korean read utterance using bidirectional long short–term memory (BLSTM)–based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without prompted text. The proposed method with the prompted text performs (a) a speech feature extraction step, (b) a forced‐alignment step using a native AM and non‐native AM, and (c) a linear regression–based proficiency scoring step for the five proficiency scores. Meanwhile, the proposed method without the prompted text additionally performs Korean speech recognition and a subword un‐segmentation for the missing text. The experimental results indicate that the proposed method with prompted text improves the performance for all scores when compared to a method employing conventional AMs. In addition, the proposed method without the prompted text has a fluency score performance comparable to that of the method with prompted text.

Keywords

• automatic speech recognition (asr) for a non‐native korean utterance
• bidirectional long short–term memory (blstm)–based acoustic models (ams)
• speech data augmentation
• spoken computer‐assisted language learning (call)
• spoken proficiency assessment