You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish
arxiv(2024)
Abstract
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.
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