Human vs AI: Comparing Transcription Performance in Dutch Older Adults' Speech
A. Weng (TU Delft - Electrical Engineering, Mathematics and Computer Science)
O.E. Scharenborg – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
B.J.W. Dudzik – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Automatic speech recognition (ASR) systems are used in voice assistants and games. Studies have shown that these ASR systems do not work well for all everyone. E.g., speech from regional accents is recognised worse than the native standard accent and speech from older adults are recognised worse than adults.However, no research has been done in comparing humans and ASR systems on Dutch older adults' speech.This paper will fill this gap by finding out the transcription performance of humans and ASR systems (Google Telephony and a conformer model with XLSR-53 features) on Dutch older adults' speech and comparing the performances with each other.This paper also investigated whether regional accents, gender, and age had a significant effect on the performance of humans and ASR systems.The results showed that humans and ASR systems perform similar to each other and that their performance are positively correlated.The results also showed that humans also do not recognise speech equally from older adults as well. These results suggests that there are speech characteristics for which both humans and ASR systems have trouble with.