# CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling and Attentive Listening in Customer Center

arXiv: https://arxiv.org/abs/2305.13713 · HTML: https://mlsanity.com/paper/2305.13713/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Conversational End-to-End TTS for Voice Agent](https://mlsanity.com/paper/2005.10438/) (2020)
- [STUDIES: Corpus of Japanese Empathetic Dialogue Speech Towards Friendly Voice Agent](https://mlsanity.com/paper/2203.14757/) (2022)
- [Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation](https://mlsanity.com/paper/2204.10020/) (2022)
- [Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History](https://mlsanity.com/paper/2206.08039/) (2022)

## Built on by (most cited first, 3 of 3)

- [Generative Expressive Conversational Speech Synthesis](https://mlsanity.com/paper/2407.21491/) (2024)
- [EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels](https://mlsanity.com/paper/2601.10033/) (2026)
- [Let Me Look at You: Advanced Facial Expression Modeling for Conversational Speech Synthesis](https://mlsanity.com/paper/2607.24430/) (2026)

## Abstract (arXiv)

We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue. The existing STUDIES corpus covers only empathetic dialogue between a teacher and student in a school. To extend the application range of empathetic dialogue speech synthesis (EDSS), we designed our corpus to include the same female speaker as the STUDIES teacher, acting as an operator in simulated phone calls. We describe a corpus construction methodology and analyze the recorded speech. We also conduct EDSS experiments using the CALLS and STUDIES corpora to investigate the effect of domain differences. The results show that mixing the two corpora during training causes biased improvements in the quality of synthetic speech due to the different degrees of expressiveness. Our project page of the corpus is http://sython.org/Corpus/STUDIES-2.