HarperValleyBank: A Domain-Specific Spoken Dialog Corpus
This provides a domain-specific dataset for quick transcription experiments in banking interactions, but it is incremental as it adapts existing methods to a new dataset.
The authors introduced HarperValleyBank, a free, public spoken dialog corpus simulating consumer banking interactions with 23 hours of audio from 1,446 conversations, and provided baselines showing that tasks using their annotations are sensitive to model choice and corpus size.
We introduce HarperValleyBank, a free, public domain spoken dialog corpus. The data simulate simple consumer banking interactions, containing about 23 hours of audio from 1,446 human-human conversations between 59 unique speakers. We selected intents and utterance templates to allow realistic variation while controlling overall task complexity and limiting vocabulary size to about 700 unique words. We provide audio data along with transcripts and annotations for speaker identity, caller intent, dialog actions, and emotional valence. The data size and domain specificity makes for quick transcription experiments with modern end-to-end neural approaches. Further, we provide baselines for representation learning, adapting recent work to embed waveforms for downstream prediction tasks. Our experiments show that tasks using our annotations are sensitive to both the model choice and corpus size.