ArticleData in brief2024
IndoWaveSentiment: Indonesian audio dataset for emotion classification.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- INESCO Dataset: indonesian expressive speech corpus for emotion speech synthesis.Data in brief · 2026Article
- A validated Mandarin Chinese Auditory Emotion Database of Subject-Personal-Pronoun Sentences (MCAE-SPPS).Scientific data · 2026Article
- IndoWaveSentiment: Indonesian audio dataset for emotion classification.Data in brief · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Voice is a one of media for human communication and interaction. Emotions conveyed through voice, such as laughter or tears, can communicate messages more quickly than spoken or written language. In sentiment analysis, the emotional component is crucial for reflecting human perceptions and opinions. This paper introduces IndoWaveSentiment, a dataset of emotional voice recordings categorized into five classes: neutral, happy, surprised, disgusted, and disappointed. The data collection took place in a recording studio with ten actors, evenly split between men and women. Each actor repeated the same sentence in Bahasa Indonesia three times for each emotion class, and the recordings were saved in .wav format. The annotation process was manually conducted using Audacity and validated through a questionnaire-based sampling technique that supports audio data. This dataset is valuable for researchers in Signal Processing and Artificial Intelligence, aiding the development of classification models within Machine Learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.