Speech emotion recognition system using features and text.
problem Improving accuracy in emotion recognition from speech.
method Used speech features (Spectrogram, MFCC) and text, trained Deep Neural Networks.
result Combined MFCC-Text CNN model achieved highest accuracy.
Method converts emotions in nonparallel speech data.
problem Lack of parallel data for speech emotion conversion.
method Unsupervised style transfer technique for nonparallel training.
result Effectiveness demonstrated on nonparallel corpora with four emotions.
Deep architecture learns transferable features for robust speech emotion recognition.
problem Robust and discriminative features for diverse speech emotion domains.
method Jointly uses CNN for domain-shared features and LSTM for domain-specific emotion classification.
result Transferable features provide gains up to 18.4% in speech emotion recognition.
This research improves emotion detection from speech, enhancing CCC by 30%.
problem Improving emotion detection from speech for categorical emotions.
method Used LSTM and TC-LSTM networks, trained with multiple datasets and robust features.
result Improved CCC for valence by 30% compared to baseline.
VAE learns latent speech emotion features for classification.
problem Learning effective features for speech emotion recognition.
method Variational Autoencoders (VAEs) for generating latent speech emotion representations.
result VAEs produce state-of-the-art results for speech emotion classification.
Improved speech emotion recognition using pre-trained language models.
problem Challenging task of speech emotion recognition for natural human-machine interaction.
method Fine-tuning pre-trained language models for text emotion recognition, combining with speech emotion recognition.
result 73.5% accuracy in speech emotion recognition on a subset of IEMOCAP dataset.
Study on mother-infant affect communication using audio recordings.
problem Lack of accurate emotional speech databases for real-life settings.
method Used RAVDESS database and trained a Convolutional Neural Nets model.
result Dominant emotions in mother-infant speech were angry and sad.
ResNet with Focal Loss improves speech emotion recognition.
problem Speech emotion recognition using plain text features is insufficient.
method Residual Convolutional Neural Network (ResNet) trained with Focal Loss.
result Focal Loss enhances model's focus on hard examples.
Paper proposes a CNN for speech emotion recognition using center loss and reconstruction.
problem Speech emotion recognition (SER) in audio signals.
method Convolutional Neural Network (CNN) with center loss and reconstruction as regularizers.
result Proposed method achieves highly discriminative features for SER.
This work tackles domain shift in speech emotion recognition by proposing class-wise adversarial domain adaptation.
problem Domain shift between corpora poses a challenge for speech emotion recognition, especially for positive/negative emotions.
method Class-wise adversarial domain adaptation to reduce shift between different corpora.
result Our method is effective even with limited target labeled examples, as demonstrated on EMODB and Aibo corpora.
Enhances speech emotion recognition using transfer learning.
problem Improving accuracy in speech emotion recognition.
method Transformer-based Predictive Coding with transfer learning.
result Significantly improved emotion recognition accuracy.
Improved anger detection in speech using transfer learning from SoundNet.
problem Detecting anger in speech with limited emotion datasets.
method Transfer learning from SoundNet, a multimodal audio classifier trained on video data.
result Improved performance and generalization on various speech emotion datasets.
Improved emotional speech classification using deep learning.
problem Difficult to assess subtle emotional changes in speech.
method Deep Convolutional Generative Adversarial Networks (DCGAN) for unlabeled data, multitask learning.
result Speaker-independent classification performance improved to 43.88% on a 5-point scale and 49.80% on a 3-point scale.
End-to-end deep learning detects emotions in real-life emergency calls.
problem Recognizing emotions in real-life emergency call center recordings.
method Used an end-to-end deep learning architecture trained on IEMOCAP and CEMO datasets.
result Obtained 45.6% Unweighted Accuracy Recall on CEMO with 4 classes, 76.9% on 2 classes (Anger, Neutral).
Paper explores deep learning features for complex emotion recognition.
problem Improving emotion recognition accuracy in complex emotions.
method Used pretrained networks (AudioSet Net, VoxCeleb Net, Deep Speech Net) and their deep layer features for emotion recognition.
result Achieved highest F1 score of 0.85 on EmoReact dataset.
Approach to detect emotion from speech using majority voting and selected features.
problem Detecting human emotion from speech.
method Majority voting technique over machine learning models (NN, DT, SVM, KNN).
result Majority voting technique achieves better accuracy than individual models.
Enhances speech emotion recognition by adapting to varying time scales.
problem Robust emotion recognition from speech audio with temporal variations.
method Introduces multi-time-scale (MTS) convolutional layers to CNNs.
result MTS layers improve generalization, especially on smaller datasets.
Speech emotion recognition improved with simpler machine learning models.
problem Identifying emotions from speech is ambiguous and challenging.
method Feature-engineering approach using hand-crafted audio features and text features. Comparison of traditional machine learning and deep learning models.
result Lighter machine learning models outperform deep learning models for emotion recognition.
Enhances speech emotion recognition by integrating visual data with attention mechanisms.
problem Improving emotion detection accuracy by combining multiple modalities.
method Introducing an attention mechanism to combine audio, text, and video modalities.
result Significant improvement of 3.65% in weighted accuracy.
ADDoG improves cross-dataset speech emotion recognition.
problem Cross-dataset speech emotion recognition failure.
method ADDoG uses an iterative approach to move representations closer together across datasets.
result ADDoG and MADDoG improve cross-dataset speech emotion recognition.
Novel bio-inspired masking for robust speech emotion recognition.
problem Noise degradation in speech emotion recognition.
method Cochlear cepstrogram-based contrastive learning with temporal and frequency masking.
result Improved speech emotion recognition performance on K-EmoCon benchmark.
Bardo Composer generates tabletop RPG music based on player speech.
problem Creating immersive background music for tabletop RPGs.
method Speech recognition, emotion classification, and music generation using a novel beam search algorithm.
result Generated music pieces can be accurately identified by human subjects as conveying the intended emotion.
Enhanced speech emotion recognition using nonlinear recurrence dynamics.
problem Improving speech emotion recognition accuracy.
method Phase space reconstruction, Recurrence Plot, Recurrence Quantification Analysis, statistical functionals, feature fusion, Bidirectional Recurrent Neural Network.
result State-of-the-art performance on IEMOCAP with up to 10.7% improvement in accuracy.
Adversarial autoencoders improve speech-based emotion recognition.
problem Improving speech-based emotion recognition accuracy.
method Adversarial autoencoders map feature vectors to different noise PDFs, allowing synthetic sample generation.
result Adversarial autoencoders can encode high-dimensional feature vectors into a compressed space with minimal loss of emotion class discriminability.
Study evaluates adversarial attacks on speech emotion recognition systems.
problem Adversarial examples challenge the robustness of speech emotion recognition systems.
method Proposes adversarial training and GAN as defenses.
result Demonstrates effective use of adversarial examples for robustness.
End-to-end network predicts and aligns continuous emotion labels.
problem Inconsistent alignment of continuous emotion labels with speech signals.
method Convolutional neural network with a multi-delay sinc layer.
result State-of-the-art results in predicting and aligning continuous emotion labels.
Paper explores unsupervised learning for speech synthesis control.
problem Learning control over speech output without labeled data.
method Study of unsupervised training heuristics and autoencoder models.
result Unsupervised methods can be interpreted as variational inference.
Privacy-preserving method protects user speech data from cloud services.
problem Privacy compromise in cloud-based speech analysis.
method Collects and sanitizes speech data before sharing, using transformation functions and voice conversion.
result Identification of sensitive emotional state reduced by ~96%.
Improved speech emotion recognition using pitch-synchronous single frequency filtering spectrogram.
problem Uncertainty principles in STFT spectrogram limit time and frequency resolutions.
method Modified SFF spectrogram by averaging amplitudes between GCI locations, named pitch-synchronous SFF spectrogram.
result Improved SER accuracy (63.95% to 70.4%) on IEMOCAP dataset.
Generative Adversarial Networks improve affective speech feature generation.
problem Improving feature representation for emotion recognition.
method Experimented with GAN architectures to generate feature vectors corresponding to emotions.
result GANs generate realistic synthetic samples for emotion recognition.
Improved TTS style transfer across disjoint datasets with adversarial cycle consistency.
problem Suboptimal TTS style transfer on disjoint datasets with underrepresented styles.
method Adversarial cycle consistency training with paired and unpaired triplets.
result 78% improvement in style transfer with minimal reduction in fidelity and naturalness.
FINs enhance performance in diverse datasets like finance, speech, and health.
problem Improving neural network performance across various domains.
method Feature Imitating Networks (FINs) initialize weights to approximate specific statistical features.
result FINs significantly improve performance in Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection.
Survey on DNNs for speech processing, focusing on limited data challenges.
problem Challenges in training DNNs for speech tasks with limited data.
method Overview of techniques for few-shot speech processing.
result Promising few-shot techniques for speech processing.
Study evaluates feature selection methods for emotion recognition in resource-constrained settings.
problem Reducing memory and computational requirements for emotion recognition in low-resource settings.
method Evaluation of three feature selection methods: ILFS, ReliefF, Fisher, and AFS.
result Smaller feature sets can achieve similar or better accuracy, reducing resource usage.
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
problem Automated emotion recognition from gait patterns.
method Data collection, preprocessing, and classification techniques.
result Gait patterns can indicate different emotion states, making them a promising source for emotion detection.
Deep learning predicts mental disorders from audio and text samples.
problem Predicting mental disorders from speech samples.
method Multimodal deep learning structure using various pre-trained models for audio and text embeddings, transfer learning, and auxiliary corpora.
result Acceptable accuracy in predicting mental disorders through multimodal analysis.
Unsupervised model generates distinct intonation codes for speech synthesis.
problem Lack of understanding of what prosodic variations are controlled in speech synthesis.
method Phrase-level variational autoencoder with multi-modal prior, using mode centres as intonation codes.
result Generated intonation codes are perceptually distinct and carry various affect-related styles.
Enhanced text-to-speech synthesizes expressive speech from a single example.
problem Creating a new expressive speech style from a single example of speech.
method Combines VAE and Normalizing Flows to improve disentanglement and naturalness.
result Reduces KL-divergence by 22% and improves perceptual metrics.
Improved multi-modal emotion recognition using deep learning.
problem Combining acoustic and text modalities for emotion recognition.
method Proposes a deep learning-based approach to fuse text and acoustic data using SincNet for acoustic features and parallel DCNN and Bi-RNN branches for text processing with cross attention.
result Achieves 3.5% improvement in weighted accuracy compared to existing methods.
Study categorizes and analyzes emotions in sexist tweets.
problem Lack of defined categories for sexism in NLP.
method Used a new dataset from SemEval-2018 to classify and analyze emotions in sexist tweets.
result Demonstrated the mental state and affectual state of users who tweet in different categories of sexism.
Improved method learns robust speech representations from multiple tasks.
problem Learning good speech representations without supervision.
method Single encoder with multiple self-supervised tasks.
result Transferable, robust, problem-agnostic features learned.
Paper proposes automated depression screening using deep CNNs.
problem Subjective and inconsistent depression diagnosis.
method Convolutional Neural Networks (CNN) and multipart interactive training.
result ResNet architectures achieved 77% accuracy in depression detection.
Bayesian method improves reliability of BERT for hate speech detection.
problem Reliable detection of hate speech in user-generated content.
method Bayesian approach using Monte Carlo dropout in transformer models.
result Monte Carlo dropout provides well-calibrated reliability estimates.
DDAT framework improves machine learning by dynamically adjusting difficulty.
problem Improving time-continuous emotion prediction models.
method Dynamic Difficulty Awareness Training (DDAT) framework.
result DDAT framework outperforms existing methods in emotion prediction.
Anti-transfer learning prevents misleading representations for speech tasks.
problem Misleading representations learned from orthogonal tasks in speech processing.
method Penalizes similarity between activations of a network and another trained on an orthogonal task.
result Improves classification accuracy and invariance to the orthogonal task.
This paper introduces a method to select and weight pretext tasks for better self-supervised speech representation learning.
problem Combining pretext tasks for better performance in self-supervised speech representation learning.
method Estimating calibrated weights for partial losses corresponding to pretext tasks during self-supervised training.
result Groups of selected and weighted pretext tasks perform better than classic baselines in automatic speech recognition and speaker/emotion recognition.
Hybrid f0 extraction method for various speech modes with high accuracy.
problem Reliable f0 extraction across different speech modes.
method Ordinal regression CNN and filtering/autocorrelation for pitch estimation.
result Significantly reduces pitch detection error and generalizes to unseen modes.
A neural network tackles emotion recognition, attribution, and summarization.
problem Sparsity of emotional expressions in videos.
method Bi-stream Emotion Attribution-Classification Network (BEAC-Net) with two networks: attribution and classification.
result Superior performance on emotion attribution, recognition, and summarization tasks.