“You don’t understand me!”: Comparing ASR Results for L1 and L2 Speakers of Swedish

• DOI: 10.21437/interspeech.2021-2140
• Publication Date: 2021-08-27T05:59:39Z
• Authors: Ronald Cumbal; Birger Moell; José Lopes; Olov Engwall
• Keywords: FOS: Computer and information sciences; Other Engineering and Technologies; Sound (cs.SD); Computer Science - Computation and Language; automatic speech recognition; 02 engineering and technology; language learning; Computer Science - Sound; non-native speech; Audio and Speech Processing (eess.AS); FOS: Electrical engineering, electronic engineering, information engineering; 0202 electrical engineering, electronic engineering, information engineering; Annan teknik; Computation and Language (cs.CL); Electrical Engineering and Systems Science - Audio and Speech Processing

The performance of Automatic Speech Recognition (ASR) systems has constantly increased in state-of-the-art development. However, performance tends to decrease considerably in more challenging conditions (e.g., background noise, multiple speaker social conversations) and with more atypical speakers (e.g., children, non-native speakers or people with speech disorders), which signifies that general improvements do not necessarily transfer to applications that rely on ASR, e.g., educational software for younger students or language learners. In this study, we focus on the gap in performance between recognition results for native and non-native, read and spontaneous, Swedish utterances transcribed by different ASR services. We compare the recognition results using Word Error Rate and analyze the linguistic factors that may generate the observed transcription errors.