# Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpus

arXiv: https://arxiv.org/abs/2505.17833 · HTML: https://mlsanity.com/paper/2505.17833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 4 of 4)

- [Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation](https://mlsanity.com/paper/2210.13248/) (2022)
- [LSSED: a large-scale dataset and benchmark for speech emotion recognition](https://mlsanity.com/paper/2102.01754/) (2021)
- [ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets](https://mlsanity.com/paper/2406.10275/) (2024)
- [Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks](https://mlsanity.com/paper/2203.12906/) (2022)

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

- [Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation](https://mlsanity.com/paper/2603.26592/) (2026)

## Abstract (arXiv)

Study of affect in speech requires suitable data, as emotional expression and perception vary across languages. Until now, no corpus has existed for natural expression of affect in spontaneous Finnish, existing data being acted or from a very specific communicative setting. This paper presents the first such corpus, created by annotating 12,000 utterances for emotional arousal and valence, sampled from three large-scale Finnish speech corpora. To ensure diverse affective expression, sample selection was conducted with an affect mining approach combining acoustic, cross-linguistic speech emotion, and text sentiment features. We compare this method to random sampling in terms of annotation diversity, and conduct post-hoc analyses to identify sampling choices that would have maximized the diversity. As an outcome, the work introduces a spontaneous Finnish affective speech corpus and informs sampling strategies for affective speech corpus creation in other languages or domains.