Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features

• URL: http://arxiv.org/abs/2409.09511v1
• Date: Sat, 14 Sep 2024 19:18:56 GMT
• Title: Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features
• Authors: Satvik Dixit, Daniel M. Low, Gasser Elbanna, Fabio Catania, Satrajit S. Ghosh,
• Abstract summary: 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.
• Score: 5.678610585849838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition (SER). However, unlike acoustic features with clear physical meaning, these embeddings lack clear interpretability. Explaining these embeddings is crucial for building trust in healthcare and security applications and advancing the scientific understanding of the acoustic information that is encoded in them. This paper proposes a modified probing approach to explain deep learning embeddings in the SER space. We predict interpretable acoustic features (e.g., f0, loudness) from (i) the complete set of embeddings and (ii) a subset of the embedding dimensions identified as most important for predicting each emotion. If the subset of the most important dimensions better predicts a given emotion than all dimensions and also predicts specific acoustic features more accurately, we infer those acoustic features are important for the embedding model for the given task. We conducted experiments using the WavLM embeddings and eGeMAPS acoustic features as audio representations, applying our method to the RAVDESS and SAVEE emotional speech datasets. Based on this evaluation, we demonstrate that Energy, Frequency, Spectral, and Temporal categories of acoustic features provide diminishing information to SER in that order, demonstrating the utility of the probing classifier method to relate embeddings to interpretable acoustic features.

Related papers

• 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)
• Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition [27.098672790099304]
  It has been assumed that emotion information is indirectly embedded within speaker embeddings, leading to their under-utilization. Our study reveals a direct and useful link between emotion and state-of-the-art speaker embeddings in the form of intra-speaker clusters. We introduce a novel contrastive pretraining approach applied to emotion-unlabeled data for speech emotion recognition.
  arXiv  Detail & Related papers  (2024-01-19T20:31:53Z)
• Learning Separable Hidden Unit Contributions for Speaker-Adaptive Lip-Reading [73.59525356467574]
  A speaker's own characteristics can always be portrayed well by his/her few facial images or even a single image with shallow networks. Fine-grained dynamic features associated with speech content expressed by the talking face always need deep sequential networks. Our approach consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2023-10-08T07:48:25Z)
• 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)
• Improving Speaker Diarization using Semantic Information: Joint Pairwise Constraints Propagation [53.01238689626378]
  We propose a novel approach to leverage semantic information in speaker diarization systems. We introduce spoken language understanding modules to extract speaker-related semantic information. We present a novel framework to integrate these constraints into the speaker diarization pipeline.
  arXiv  Detail & Related papers  (2023-09-19T09:13:30Z)
• Deep Feature Learning for Medical Acoustics [78.56998585396421]
  The purpose of this paper is to compare different learnables in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies.
  arXiv  Detail & Related papers  (2022-08-05T10:39:37Z)
• Self-supervised speech unit discovery from articulatory and acoustic features using VQ-VAE [2.771610203951056]
  This study examines how articulatory information can be used for discovering speech units in a self-supervised setting. We used vector-quantized variational autoencoders (VQ-VAE) to learn discrete representations from articulatory and acoustic speech data. Experiments were conducted on three different corpora in English and French.
  arXiv  Detail & Related papers  (2022-06-17T14:04:24Z)
• Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition [159.9312272042253]
  Wav-BERT is a cooperative acoustic and linguistic representation learning method. We unify a pre-trained acoustic model (wav2vec 2.0) and a language model (BERT) into an end-to-end trainable framework.
  arXiv  Detail & Related papers  (2021-09-19T16:39:22Z)
• Deep Learning for Prominence Detection in Children's Read Speech [13.041607703862724]
  We consider a labeled dataset of children's reading recordings for the speaker-independent detection of prominent words. A previous well-tuned random forest ensemble predictor is replaced by an RNN sequence to exploit potential context dependency. Deep learning is applied to obtain word-level features from low-level acoustic contours of fundamental frequency, intensity and spectral shape.
  arXiv  Detail & Related papers  (2021-04-12T14:15:08Z)
• Joint Blind Room Acoustic Characterization From Speech And Music Signals Using Convolutional Recurrent Neural Networks [13.12834490248018]
  Reverberation time, clarity, and direct-to-reverberant ratio are acoustic parameters that have been defined to describe reverberant environments. Recent audio combined with machine learning suggests that one could estimate those parameters blindly using speech or music signals. We propose a robust end-to-end method to achieve blind joint acoustic parameter estimation using speech and/or music signals.
  arXiv  Detail & Related papers  (2020-10-21T17:41:21Z)
• 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)

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.