Article URL: https://github.com/Kayvan-Zahiri/asr-age-gap Comments URL: https://news.ycombinator.com/item?id=49204388 Points: 13 # Comments: 7

Voice agents are being pointed at elderly callers, and the assumed risk is that speech recognition will not hear them. That assumption is wrong, and it is hiding the failure that is actually happening. Measured on 2,760 Common Voice clips, matched between age brackets on accent, gender and speaker so the only thing varying is age, and checked against a second 3,189-clip draw that controls for none of it: Whisper transcribes older speakers more accurately, not less. And where a stack endpoints on a fixed silence threshold, those same speakers get talked over two to two and a half times as often. That second finding has a caveat that arrived after publication and is worth reading before quoting the number: a semantic turn model closes most of the gap. Measured against Pipecat's smart-turn v3 on the same clips, +11.6pp becomes +5.9pp and stops excluding zero (section 3). The fixed-threshold result describes a real and common configuration, not every configuration. Deletions in particular do not rise, which is the result you would expect if quiet or breathy speech were being dropped. It is not being dropped. It is not a Whisper artifact. The obvious objection is that Whisper's decoder is a language model, so it might be repairing older speakers' word choices rather than hearing them better. So the same clips were re-run through wav2vec2, which is pure CTC: frame-wise, greedy, no decoder and no implicit LM. Absolute WER is much higher for wav2vec2 (LibriSpeech-only training, no LM), so only the between-bracket comparison transfers. The effect is larger there and still excludes zero, which puts it in the acoustics rather than in a decoder. A voice agent decides the caller has finished by waiting for a fixed stretch of silence. A pause inside an utterance that exceeds that threshold is heard as the end of the turn, and the agent starts talking over someone mid-sentence.