Investigating the Impact of Word Informativeness on Speech Emotion Recognition

• URL: http://arxiv.org/abs/2506.02239v1
• Date: Mon, 02 Jun 2025 20:30:48 GMT
• Title: Investigating the Impact of Word Informativeness on Speech Emotion Recognition
• Authors: Sofoklis Kakouros,
• Abstract summary: This research investigates the use of word informativeness, derived from a pre-trained language model, to identify semantically important segments.<n> Acoustic features are then computed exclusively for these identified segments, enhancing emotion recognition accuracy.
• Score: 0.38073142980732994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In emotion recognition from speech, a key challenge lies in identifying speech signal segments that carry the most relevant acoustic variations for discerning specific emotions. Traditional approaches compute functionals for features such as energy and F0 over entire sentences or longer speech portions, potentially missing essential fine-grained variation in the long-form statistics. This research investigates the use of word informativeness, derived from a pre-trained language model, to identify semantically important segments. Acoustic features are then computed exclusively for these identified segments, enhancing emotion recognition accuracy. The methodology utilizes standard acoustic prosodic features, their functionals, and self-supervised representations. Results indicate a notable improvement in recognition performance when features are computed on segments selected based on word informativeness, underscoring the effectiveness of this approach.

Related papers

• Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 [64.59170359368699]
  We present a robust system for the INTERSPEECH 2025 Speech Emotion Recognition in Naturalistic Conditions Challenge.<n>Our method combines state-of-the-art audio models with text features enriched by prosodic and spectral cues.
  arXiv  Detail & Related papers  (2025-06-02T13:46:02Z)
• Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features [5.678610585849838]
  Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition. Unlike acoustic features with clear physical meaning, these embeddings lack clear interpretability. This paper proposes a modified probing approach to explain deep learning embeddings in the speech emotion space.
  arXiv  Detail & Related papers  (2024-09-14T19:18:56Z)
• Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition [54.952250732643115]
  We study Acoustic Word Embeddings (AWEs), a fixed-length feature derived from continuous representations, to explore their advantages in specific tasks. AWEs have previously shown utility in capturing acoustic discriminability. Our findings underscore the acoustic context conveyed by AWEs and showcase the highly competitive Speech Emotion Recognition accuracies.
  arXiv  Detail & Related papers  (2024-02-04T21:24:54Z)
• Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations [2.0653090022137697]
  We explore the use of pre-trained speech representations as a form of transfer learning towards AlloSat corpus. Our experiments confirm the large gain in performance obtained with the use of pre-trained features. Surprisingly, we found that the linguistic content is clearly the major contributor for the prediction of satisfaction.
  arXiv  Detail & Related papers  (2023-10-06T10:22:51Z)
• Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition [2.223733768286313]
  We present speech features enhancement strategy that improves speech emotion recognition. The strategy is compared with the state-of-the-art methods used in the literature. Our method achieved an average recognition gain of 11.5% for six out of seven emotions for the EMO-DB dataset, and 13.8% for seven out of eight emotions for the RAVDESS dataset.
  arXiv  Detail & Related papers  (2022-08-19T11:29:03Z)
• Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training [60.825471653739555]
  We show that phonetic domain factors play an important role during pre-training while grammatical and syntactic factors are far less important. This is the first study to better understand the domain characteristics of pre-trained sets in self-supervised pre-training for speech.
  arXiv  Detail & Related papers  (2022-03-01T17:40:51Z)
• Deep Learning For Prominence Detection In Children's Read Speech [13.041607703862724]
  We present a system that operates on segmented speech waveforms to learn features relevant to prominent word detection for children's oral fluency assessment. The chosen CRNN (convolutional recurrent neural network) framework, incorporating both word-level features and sequence information, is found to benefit from the perceptually motivated SincNet filters.
  arXiv  Detail & Related papers  (2021-10-27T08:51:42Z)
• An Attribute-Aligned Strategy for Learning Speech Representation [57.891727280493015]
  We propose an attribute-aligned learning strategy to derive speech representation that can flexibly address these issues by attribute-selection mechanism. Specifically, we propose a layered-representation variational autoencoder (LR-VAE), which factorizes speech representation into attribute-sensitive nodes. Our proposed method achieves competitive performances on identity-free SER and a better performance on emotionless SV.
  arXiv  Detail & Related papers  (2021-06-05T06:19:14Z)
• Embedded Emotions -- A Data Driven Approach to Learn Transferable Feature Representations from Raw Speech Input for Emotion Recognition [1.4556324908347602]
  We investigate the applicability of transferring knowledge learned from large text and audio corpora to the task of automatic emotion recognition. Our results show that the learned feature representations can be effectively applied for classifying emotions from spoken language.
  arXiv  Detail & Related papers  (2020-09-30T09:18:31Z)
• An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation [57.68765353264689]
  Speech enhancement and speech separation are two related tasks. Traditionally, these tasks have been tackled using signal processing and machine learning techniques. Deep learning has been exploited to achieve strong performance.
  arXiv  Detail & Related papers  (2020-08-21T17:24:09Z)
• Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention [70.82604384963679]
  This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features. We extract a speaker representation used for adaptation directly from the test utterance.
  arXiv  Detail & Related papers  (2020-02-14T05:05:36Z)

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.