You Don’t Need to Speak, You Need to Listen: Robot Interaction and Human-Like Turn-Taking

Aylett, Matthew P.; Romeo, Marta

doi:10.1145/3571884.3603750

Cited by 4 publications

(1 citation statement)

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“…The most widely used datasets for training ASR systems still consist mostly of monologic read speech in well-resourced languages. For ASR systems to gain headway in truly interactive settings, they need to be exposed to more data that is closer to everyday language use in terms of linguistic diversity, conversational style, and participation (Aylett and Romeo, 2023). Fortunately, such data is available for an ever-wider range of languages .…”

Section: Recommendationsmentioning

confidence: 99%

The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems

Liesenfeld,

Lopez,

Dingemanse

2023

Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue

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Speech recognition systems are a key intermediary in voice-driven human-computer interaction. Although speech recognition works well for pristine monologic audio, real-life use cases in open-ended interactive settings still present many challenges. We argue that timing is mission-critical for dialogue systems, and evaluate 5 major commercial ASR systems for their conversational and multilingual support. We find that word error rates for natural conversational data in 6 languages remain abysmal, and that overlap remains a key challenge (study 1). This impacts especially the recognition of conversational words (study 2), and in turn has dire consequences for downstream intent recognition (study 3). Our findings help to evaluate the current state of conversational ASR, contribute towards multidimensional error analysis and evaluation, and identify phenomena that need most attention on the way to build robust interactive speech technologies.

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Section: Recommendationsmentioning

confidence: 99%