Filler words in speech-to-text: retained vs clean output

Deepgram’s `filler_words` setting affects seven detected speech tokens but, by default, documentation indicates that it removes only “uh” and “um,” while at least “uh-huh” can remain in transcripts and the behavior of other backchannel sounds is unclear. This distinction matters because “uh” and “um” generally signal hesitation, whereas tokens such as “uh-huh,” “uh-uh,” “mhmm,” and “mm-mm” may communicate agreement, disagreement, or listener attention, potentially altering the meaning of legal records, research data, clinical interactions, meeting summaries, and language-model inputs. The recommended setting depends on the use case: verbatim-sensitive contexts such as court reporting, conversation research, and speaking coaching may require retaining tokens, while clinical documentation, captions, and recaps often favor cleaner output. Because Deepgram’s documentation contains ambiguities and conflicting statements about model support, language coverage, and retained backchannels, developers are advised to test their exact model and configuration, preserve raw transcripts where possible, and explicitly process any remaining non-lexical tokens downstream.

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