Automatic speech recognition systems can be less accurate for children than for older
speakers. For Dutch, previous evaluations found higher word error rates for native children than for native teenagers, but they did not show which types of words contribute to the difference. This paper studies whether that difference is concentrated in particular word categories. A word category is the part of speech assigned to a word in the reference transcript, for example noun, verb, pronoun, article, or preposition. The analysis uses native Dutch child and teenager speech from the JASMIN-CGN corpus in two speaking styles: read speech and speech from human machine interaction tasks. The analysis compares Google Chirp 2 and Whisper-large-v3 outputs with the reference transcripts and groups substitutions and deletions by word category. Insertions are reported separately because an inserted system word has no matching reference word category. Children have higher word error rates than teenagers in every condition. The difference is about 9 percentage points in read speech and about 4 to 5 percentage points in human machine interaction speech. In read speech, most of the substitution and deletion difference comes from nouns, verbs, and pronouns. In human machine interaction speech, errors are spread across categories, so no single category explains much of the difference. A control that compares utterances with the same counts of word categories reduces the verb and pronoun differences, while the noun difference remains largest. A check of the specific words behind these categories shows why this matters: many noun and verb errors come from words found only in the child read reference transcripts, whereas pronoun errors mostly involve words used by both groups. The results show that word categories are useful for locating where the error rate difference appears, but they are not a complete explanation without checking the specific words and speaking material behind
each category. ... Read more