# HarperValleyBank: A Domain-Specific Spoken Dialog Corpus

arXiv: https://arxiv.org/abs/2010.13929 · HTML: https://mlsanity.com/paper/2010.13929/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks](https://mlsanity.com/paper/2212.10525/) (2022)
- [Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models](https://mlsanity.com/paper/2202.13155/) (2022)
- [Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems](https://mlsanity.com/paper/2203.00006/) (2022)
- [Integrating Dialog History into End-to-End Spoken Language Understanding Systems](https://mlsanity.com/paper/2108.08405/) (2021)
- [Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding](https://mlsanity.com/paper/2204.05169/) (2022)
- [Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems](https://mlsanity.com/paper/2104.01287/) (2021)
- [On Building Spoken Language Understanding Systems for Low Resourced Languages](https://mlsanity.com/paper/2205.12818/) (2022)
- [Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History](https://mlsanity.com/paper/2305.00926/) (2023)
- [Cascaded Cross-Modal Transformer for Audio-Textual Classification](https://mlsanity.com/paper/2401.07575/) (2024)

## Abstract (arXiv)

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.