Speech Emotion Recognition Using Quaternion Convolutional Neural Networks

• URL: http://arxiv.org/abs/2111.00404v1
• Date: Sun, 31 Oct 2021 04:06:07 GMT
• Title: Speech Emotion Recognition Using Quaternion Convolutional Neural Networks
• Authors: Aneesh Muppidi and Martin Radfar
• Abstract summary: This paper proposes a quaternion convolutional neural network (QCNN) based speech emotion recognition model. Mel-spectrogram features of speech signals are encoded in an RGB quaternion domain. The model achieves an accuracy of 77.87%, 70.46%, and 88.78% for the RAVDESS, IEMOCAP, and EMO-DB datasets, respectively.
• Score: 1.776746672434207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although speech recognition has become a widespread technology, inferring emotion from speech signals still remains a challenge. To address this problem, this paper proposes a quaternion convolutional neural network (QCNN) based speech emotion recognition (SER) model in which Mel-spectrogram features of speech signals are encoded in an RGB quaternion domain. We show that our QCNN based SER model outperforms other real-valued methods in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS, 8-classes) dataset, achieving, to the best of our knowledge, state-of-the-art results. The QCNN also achieves comparable results with the state-of-the-art methods in the Interactive Emotional Dyadic Motion Capture (IEMOCAP 4-classes) and Berlin EMO-DB (7-classes) datasets. Specifically, the model achieves an accuracy of 77.87\%, 70.46\%, and 88.78\% for the RAVDESS, IEMOCAP, and EMO-DB datasets, respectively. In addition, our results show that the quaternion unit structure is better able to encode internal dependencies to reduce its model size significantly compared to other methods.

Related papers

• Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition [0.0]
  Speech emotion recognition (SER) classifies human emotions in speech with a computer model. We propose a 6-layer convolutional neural network (CNN) model with efficient channel attention (ECA) to pursue an efficient model structure. With the interactive emotional dyadic motion capture (IEMOCAP) dataset, increasing the frequency resolution in preprocessing emotional speech can improve emotion recognition performance.
  arXiv  Detail & Related papers  (2024-09-06T03:17:25Z)
• EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks [0.0]
  This research explores the integration of deep learning techniques in speech emotion recognition. It introduces a framework that combines a pre-existing speaker diarization pipeline and an emotion identification model built on a Convolutional Neural Network (CNN) The proposed model yields an unweighted accuracy of 63%, demonstrating remarkable efficiency in accurately identifying emotional states within speech signals.
  arXiv  Detail & Related papers  (2023-10-19T16:02:53Z)
• Evaluating raw waveforms with deep learning frameworks for speech emotion recognition [0.0]
  We represent a model, which feeds raw audio files directly into the deep neural networks without any feature extraction stage. We use six different data sets, EMO-DB, RAVDESS, TESS, CREMA, SAVEE, and TESS+RAVDESS. The proposed model performs 90.34% of accuracy for EMO-DB with CNN model, 90.42% of accuracy for RAVDESS, 99.48% of accuracy for TESS with LSTM model, 69.72% of accuracy for CREMA with CNN model, 85.76% of accuracy for SAVEE with CNN model in
  arXiv  Detail & Related papers  (2023-07-06T07:27:59Z)
• From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks [82.21746840893658]
  This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. We show that while the ResNet-18 model trained on DWT spectrograms achieves a high recognition accuracy, attacking this model is relatively more costly for the adversary.
  arXiv  Detail & Related papers  (2022-04-14T15:14:08Z)
• Learning Speech Emotion Representations in the Quaternion Domain [16.596137913051212]
  RH-emo is a novel semi-supervised architecture aimed at extracting quaternion embeddings from real-valued monoaural spectrograms. RH-emo is a hybrid real/quaternion autoencoder network that consists of a real-valued encoder in parallel to a real-valued emotion classifier and a quaternion-valued decoder. We test our approach on speech emotion recognition tasks using four popular datasets: Iemocap, Ravdess, EmoDb and Tess.
  arXiv  Detail & Related papers  (2022-04-05T17:45:09Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition [52.71604809100364]
  We propose wav2vec-Switch, a method to encode noise robustness into contextualized representations of speech. Specifically, we feed original-noisy speech pairs simultaneously into the wav2vec 2.0 network. In addition to the existing contrastive learning task, we switch the quantized representations of the original and noisy speech as additional prediction targets.
  arXiv  Detail & Related papers  (2021-10-11T00:08:48Z)
• Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention [2.8017924048352576]
  We propose a simple yet efficient neural network architecture to exploit both acoustic and lexical informationfrom speech. The proposed framework using multi-scale con-volutional layers (MSCNN) to obtain both audio and text hid-den representations. Extensive experiments show that the proposed modeloutperforms previous state-of-the-art methods on IEMOCAPdataset.
  arXiv  Detail & Related papers  (2021-06-08T06:45:42Z)
• Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks [82.54695985117783]
  We investigate the suitability of state-of-the-art deep learning architectures for continuous emotion recognition using long video sequences captured in-the-wild. We have developed and evaluated convolutional recurrent neural networks combining 2D-CNNs and long short term-memory units, and inflated 3D-CNN models, which are built by inflating the weights of a pre-trained 2D-CNN model during fine-tuning.
  arXiv  Detail & Related papers  (2020-11-18T13:42:05Z)
• Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription [73.66530509749305]
  In this paper, we argue that, even in difficult cases, some end-to-end approaches show performance close to the hybrid baseline. We experimentally compare and analyze CTC-Attention versus RNN-Transducer approaches along with RNN versus Transformer architectures. Our best end-to-end model based on RNN-Transducer, together with improved beam search, reaches quality by only 3.8% WER abs. worse than the LF-MMI TDNN-F CHiME-6 Challenge baseline.
  arXiv  Detail & Related papers  (2020-04-22T19:08:33Z)
• Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances [53.063441357826484]
  Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions. Speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks. This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances.
  arXiv  Detail & Related papers  (2020-02-14T13:34:33Z)

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.