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An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-03320-9openalex-snapshot
Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions
inferredcites →https://doi.org/10.1007/978-3-642-03320-9_5openalex-snapshot
Polish Emotional Speech Database – Recording and Preliminary Validation
inferredcites →https://doi.org/10.1007/s00034-018-0992-4openalex-snapshot
Text-Independent Speaker Recognition in Clean and Noisy Backgrounds Using Modified VQ-LBG Algorithm
inferredcites →https://doi.org/10.1007/s00034-019-01026-zopenalex-snapshot
Speaker Identification for OFDM-Based Aeronautical Communication System
inferredcites →https://doi.org/10.1007/s10579-008-9076-6openalex-snapshot
IEMOCAP: interactive emotional dyadic motion capture database
inferredcites →https://doi.org/10.1007/s10772-012-9176-yopenalex-snapshot
Robust emotional speech classification in the presence of babble noise
inferredcites →https://doi.org/10.1007/s12652-017-0644-8openalex-snapshot
Feature fusion methods research based on deep belief networks for speech emotion recognition under noise condition
inferredcites →https://doi.org/10.1016/j.asej.2016.11.001openalex-snapshot
Wavelet based feature combination for recognition of emotions
inferredcites →https://doi.org/10.1016/j.asoc.2018.10.022openalex-snapshot
A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform
inferredcites →https://doi.org/10.1016/j.bspc.2018.08.035openalex-snapshot
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
inferredcites →https://doi.org/10.1016/j.dsp.2018.11.005openalex-snapshot
Ensemble of jointly trained deep neural network-based acoustic models for reverberant speech recognition
inferredcites →https://doi.org/10.1016/j.inffus.2018.09.008openalex-snapshot
Emotion recognition using deep learning approach from audio–visual emotional big data
inferredcites →https://doi.org/10.1016/j.patrec.2009.12.036openalex-snapshot
A learning approach to hierarchical feature selection and aggregation for audio classification
inferredcites →https://doi.org/10.1016/j.sigpro.2009.09.009openalex-snapshot
Emotion recognition from speech signals using new harmony features
inferredcites →https://doi.org/10.1016/j.specom.2006.04.003openalex-snapshot
Emotional speech recognition: Resources, features, and methods
inferredcites →https://doi.org/10.1016/j.specom.2019.09.002openalex-snapshot
Automatic speech emotion recognition using an optimal combination of features based on EMD-TKEO
inferredcites →https://doi.org/10.1016/s1005-8885(17)60193-6openalex-snapshot
Noisy speech emotion recognition using sample reconstruction and multiple-kernel learning
inferredcites →https://doi.org/10.1109/5.237532openalex-snapshot
Signal modeling techniques in speech recognition
inferredcites →https://doi.org/10.1109/access.2017.2728801openalex-snapshot
Enhanced Forensic Speaker Verification Using a Combination of DWT and MFCC Feature Warping in the Presence of Noise and Reverberation Conditions
inferredcites →https://doi.org/10.1109/icassp.2002.1005751openalex-snapshot
Yet another algorithm for pitch tracking
inferredcites →https://doi.org/10.1109/icassp.2002.5743729openalex-snapshot
Yet Another Algorithm for Pitch Tracking
inferredcites →https://doi.org/10.1109/icassp.2014.6854515openalex-snapshot
A feature selection and feature fusion combination method for speaker-independent speech emotion recognition
inferredcites →https://doi.org/10.1109/icpr.2010.1132openalex-snapshot
Speech Emotion Analysis in Noisy Real-World Environment
inferredcites →https://doi.org/10.1109/icsipa.2017.8120601openalex-snapshot
Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions
inferredcites →https://doi.org/10.1109/kst.2013.6512793openalex-snapshot
Speech emotion recognition using Support Vector Machines
inferredcites →https://doi.org/10.1109/sice.2006.314794openalex-snapshot
Robust Speech Emotion Recognition Using Log Frequency Power Ratio
inferredcites →https://doi.org/10.1109/tassp.1980.1163420openalex-snapshot
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
inferredcites →https://doi.org/10.1109/tpami.2008.52openalex-snapshot
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
inferredcites →https://doi.org/10.1109/tsa.2004.838534openalex-snapshot
Toward detecting emotions in spoken dialogs
inferredcites →https://doi.org/10.1111/j.1469-1809.1936.tb02137.xopenalex-snapshot
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
inferredcites →https://doi.org/10.21437/icslp.2000-743openalex-snapshot
The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions
inferredcites →https://doi.org/10.21437/interspeech.2005-446openalex-snapshot
A database of German emotional speech
inferredcites →https://doi.org/10.21437/interspeech.2010-286openalex-snapshot
Spectro-temporal modulations for robust speech emotion recognition
inferredcites →https://doi.org/10.21437/interspeech.2012-118openalex-snapshot
Emotion recognition using acoustic and lexical features
inferredcites →https://doi.org/10.21437/interspeech.2018-1353openalex-snapshot
Emotion Identification from Raw Speech Signals Using DNNs
inferredcites →https://doi.org/10.21437/interspeech.2018-2581openalex-snapshot
An End-to-End Deep Learning Framework for Speech Emotion Recognition of Atypical Individuals
inferredcites →https://doi.org/10.21437/speechprosody.2006-150openalex-snapshot
Emotion recognition in the noise applying large acoustic feature sets
inferredcites →https://doi.org/10.2478/aoa-2013-0054openalex-snapshot
Speech Emotion Recognition under White Noise
inferredcites →https://doi.org/10.3390/make1010031openalex-snapshot
A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface
inferredcites →https://doi.org/10.48550/arxiv.1303.1761openalex-snapshot
Improving Automatic Emotion Recognition from speech using Rhythm and Temporal feature
inferredcites →https://doi.org/10.5772/intechopen.84856openalex-snapshot
Automatic Speech Emotion Recognition Using Machine Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-71187-0_114openalex-snapshot
A Statistical Based Modeling Approach for Deep Learning Based Speech Emotion Recognition
inferred← citeshttps://doi.org/10.1007/978-3-030-73689-7_16openalex-snapshot
LSTM vs. GRU for Arabic Machine Translation
inferred← citeshttps://doi.org/10.1007/978-3-031-15191-0_27openalex-snapshot
Improving Speaker-Dependency/Independency of Wavelet-Based Speech Emotion Recognition
inferred← citeshttps://doi.org/10.1007/978-3-031-26852-6_35openalex-snapshot
Speech Emotion Recognition Using Pre-trained and Fine-Tuned Transfer Learning Approaches
inferred← citeshttps://doi.org/10.1007/978-981-95-1896-8_12openalex-snapshot
Role of Speech Rhythms in Shaping Meaning Inferences: An Experimental Study in Bangla
inferred← citeshttps://doi.org/10.1007/s10772-023-10038-9openalex-snapshot
Speaker and gender dependencies in within/cross linguistic Speech Emotion Recognition
inferred← citeshttps://doi.org/10.1007/s10772-026-10287-4openalex-snapshot
Attention-based Bi-LSTM model with hybrid wavelet–MFCC features for speech emotion recognition
inferred← citeshttps://doi.org/10.1007/s11042-020-09591-1openalex-snapshot
Recognition of emotion from speech using evolutionary cepstral coefficients
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A statistical feature extraction for deep speech emotion recognition in a bilingual scenario
inferred← citeshttps://doi.org/10.1007/s11042-026-21812-7openalex-snapshot
Enhancing speech emotion recognition using statistical self-supervised embeddings
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Unsupervised feature selection and NMF de-noising for robust Speech Emotion Recognition
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MI-EEGNET: A novel convolutional neural network for motor imagery classification
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A review on speech emotion recognition: A survey, recent advances, challenges, and the influence of noise
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128646openalex-snapshot
MFGCN: Multimodal fusion graph convolutional network for speech emotion recognition
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Noise-robust voice conversion with domain adversarial training
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A systematic review of interpretability and explainability for speech emotion features in automatic speech emotion recognition
inferred← citeshttps://doi.org/10.1016/j.specom.2022.02.007openalex-snapshot
CyTex: Transforming speech to textured images for speech emotion recognition
inferred← citeshttps://doi.org/10.1109/inista52262.2021.9548341openalex-snapshot
On the use of MFCC and SWT-based features for offensive speech detection in social media
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Alzheimer’s Detection via Cepstral Domain Feature Fusion and Mann-Whitney U-Test Statistical Filtering
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A deep learning framework for offensive speech detection
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A Comprehensive Study on the Effectiveness of ASR Representations for Noise-Robust Speech Emotion Recognition
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Fusion Based AER System Using Deep Learning Approach for Amplitude and Frequency Analysis
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Speech emotion recognition using wavelet packet reconstruction with attention-based deep recurrent neutral networks
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The Use of Multi-Feature Fusion in the Evaluation of Emotional Expressions in Spoken English
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Advanced feature selection and temporal attention mechanisms with Bi-LSTM classifier for optimizing emotion recognition in Kashmiri speech
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