Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results
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Computer Science > Computation and Language
Title:Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results
Abstract:Humans are often considered to be the best listeners and seen as the upper-bound performance of automatic speech recognition (ASR) systems. We present a preliminary comparison of the performances of state-of-the-art ASR systems and Dutch native listeners on the recognition of "diverse" speech, specifically Dutch child and older adults' speech and Flemish. Google Telephony outperformed the other ASR systems. Importantly, the ASR systems showed similar performance to the listeners, and in specific cases even outperformed them. Slight performance differences between the listeners and ASR systems were found related to speaker's age and regional accents and utterance length. Future research should focus on making ASR systems more robust to acoustic variability related to aging and regional accents. A comparison of ASR recognition performances on the test stimuli and the full Jasmin-CGN test sets showed the influence of the specific test sets on the conclusions regarding benchmarking human and ASR performance.
| Comments: | 7 pages, 4 figures |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.19049 [cs.CL] |
| (or arXiv:2607.19049v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19049
arXiv-issued DOI via DataCite (pending registration)
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