# Hi,KIA: A Speech Emotion Recognition Dataset for Wake-Up Words

arXiv: https://arxiv.org/abs/2211.03371 · HTML: https://mlsanity.com/paper/2211.03371/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Emotional speech synthesis and recognition, one of 11 directions in Speech and audio language models.
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Placed in

- Speech and audio language models / [Emotional speech synthesis and recognition](https://mlsanity.com/speech/emotional-speech-synthesis-and-recognition/)

## Builds on (most cited first, 6 of 6)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition](https://mlsanity.com/paper/2110.06309/) (2021)
- [Adversarial Music: Real World Audio Adversary Against Wake-word Detection System](https://mlsanity.com/paper/1911.00126/) (2019)
- [HI-MIA : A Far-field Text-Dependent Speaker Verification Database and the Baselines](https://mlsanity.com/paper/1912.01231/) (2019)
- [On Front-end Gain Invariant Modeling for Wake Word Spotting](https://mlsanity.com/paper/2010.06676/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning Speech Attribute Representations via Multi-Teacher Distillation](https://mlsanity.com/paper/2610.21941/) (2026)

## Abstract (arXiv)

Wake-up words (WUW) is a short sentence used to activate a speech recognition system to receive the user's speech input. WUW utterances include not only the lexical information for waking up the system but also non-lexical information such as speaker identity or emotion. In particular, recognizing the user's emotional state may elaborate the voice communication. However, there is few dataset where the emotional state of the WUW utterances is labeled. In this paper, we introduce Hi, KIA, a new WUW dataset which consists of 488 Korean accent emotional utterances collected from four male and four female speakers and each of utterances is labeled with four emotional states including anger, happy, sad, or neutral. We present the step-by-step procedure to build the dataset, covering scenario selection, post-processing, and human validation for label agreement. Also, we provide two classification models for WUW speech emotion recognition using the dataset. One is based on traditional hand-craft features and the other is a transfer-learning approach using a pre-trained neural network. These classification models could be used as benchmarks in further research.