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

• URL: http://arxiv.org/abs/2505.17833v1
• Date: Fri, 23 May 2025 12:47:21 GMT
• Title: Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpus
• Abstract summary: 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.<n>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.
• Score: 8.008453432425364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Looking for Affect in Spontaneous Finnish Speech through Linguistic Interpretability [4.94997283141601]
  We show that the combination of text- and audio-based features improves valence regression results over the individual modalities.<n>Results support prior findings from other languages, providing new data and knowledge on spontaneous Finnish speech.
  arXiv  Detail & Related papers  (2026-07-27T08:32:40Z)
• iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis [7.298729249943839]
  iMiGUE-Speech is an extension of the iMiGUE dataset that provides a spontaneous affective corpus for studying emotional and affective states.<n>iMiGUE-Speech captures spontaneous affect arising naturally from real match outcomes.
  arXiv  Detail & Related papers  (2026-02-25T00:38:19Z)
• Language-Agnostic Analysis of Speech Depression Detection [2.5764071253486636]
  This work analyzes automatic speech-based depression detection across two languages, English and Malayalam. A CNN model is trained to identify acoustic features associated with depression in speech, focusing on both languages. Our findings and collected data could contribute to the development of language-agnostic speech-based depression detection systems.
  arXiv  Detail & Related papers  (2024-09-23T07:35:56Z)
• Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation [23.757896930482342]
  This work explores the selection process through a study of downstream tasks. Units that perform well in resynthesis performance do not necessarily correlate with those that enhance translation efficacy.
  arXiv  Detail & Related papers  (2024-07-08T08:53:26Z)
• Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue [71.15186328127409]
  Paralinguistics-enhanced Generative Pretrained Transformer (ParalinGPT) Model takes the conversational context of text, speech embeddings, and paralinguistic attributes as input prompts within a serialized multitasking framework. We utilize the Switchboard-1 corpus, including its sentiment labels as the paralinguistic attribute, as our spoken dialogue dataset.
  arXiv  Detail & Related papers  (2023-12-23T18:14:56Z)
• AffectEcho: Speaker Independent and Language-Agnostic Emotion and Affect Transfer for Speech Synthesis [13.918119853846838]
  Affect is an emotional characteristic encompassing valence, arousal, and intensity, and is a crucial attribute for enabling authentic conversations. We propose AffectEcho, an emotion translation model, that uses a Vector Quantized codebook to model emotions within a quantized space. We demonstrate the effectiveness of our approach in controlling the emotions of generated speech while preserving identity, style, and emotional cadence unique to each speaker.
  arXiv  Detail & Related papers  (2023-08-16T06:28:29Z)
• Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data [0.0]
  We propose a method for speech-to-speech emotion translation that operates at the level of discrete speech units. We show that this embedding can be used to predict the pitch and duration of speech units in a target language. We evaluate our approach to English and French speech signals and show that it outperforms a baseline method.
  arXiv  Detail & Related papers  (2023-06-29T08:06:54Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.<n>We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.<n>Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• EMNS /Imz/ Corpus: An emotive single-speaker dataset for narrative storytelling in games, television and graphic novels [6.2375553155844266]
  The Emotive Narrative Storytelling (EMNS) corpus is a unique speech dataset created to enhance conversations' emotive quality. It consists of a 2.3-hour recording featuring a female speaker delivering labelled utterances. It encompasses eight acted emotional states, evenly distributed with a variance of 0.68%, along with expressiveness levels and natural language descriptions with word emphasis labels.
  arXiv  Detail & Related papers  (2023-05-22T15:32:32Z)
• Textless Speech Emotion Conversion using Decomposed and Discrete Representations [49.55101900501656]
  We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion. First, we modify the speech content by translating the content units to a target emotion, and then predict the prosodic features based on these units. Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder.
  arXiv  Detail & Related papers  (2021-11-14T18:16:42Z)
• EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model [56.75775793011719]
  We introduce and publicly release a Mandarin emotion speech dataset including 9,724 samples with audio files and its emotion human-labeled annotation. Unlike those models which need additional reference audio as input, our model could predict emotion labels just from the input text and generate more expressive speech conditioned on the emotion embedding. In the experiment phase, we first validate the effectiveness of our dataset by an emotion classification task. Then we train our model on the proposed dataset and conduct a series of subjective evaluations.
  arXiv  Detail & Related papers  (2021-06-17T08:34:21Z)
• Leveraging Pre-trained Language Model for Speech Sentiment Analysis [58.78839114092951]
  We explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis. We propose a pseudo label-based semi-supervised training strategy using a language model on an end-to-end speech sentiment approach.
  arXiv  Detail & Related papers  (2021-06-11T20:15:21Z)
• Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training [91.95855310211176]
  Emotional voice conversion aims to change the emotional state of an utterance while preserving the linguistic content and speaker identity. We propose a novel 2-stage training strategy for sequence-to-sequence emotional voice conversion with a limited amount of emotional speech data. The proposed framework can perform both spectrum and prosody conversion and achieves significant improvement over the state-of-the-art baselines in both objective and subjective evaluation.
  arXiv  Detail & Related papers  (2021-03-31T04:56:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.