Speech Emotion Recognition Using Machine Learning Thesis Topics
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- Speech emotion recognition for psychotherapy: an analysis of traditional machine learning and deep learning techniques
Keywords:
speech, emotion recognition, Machine Learning, MFCCs, deep learning, Boosting, CNN, LSTM
Our paper compares the application of traditional ML and DL methods were directed using spectral characters like Mel-frequency cepstral coefficients on merged dataset of multiple audio file resources like REVDESS, TESS AND SAVEE. Our paper uses Random Forest classifier for predict the total accuracy. DL methods like LSTM and CNN are also compared with traditional ML methods.
- Machine Learning based Speech Emotion Recognition in Hindi Audio
Keywords:
Support Vector Classifier, Random Forest, Logistic Regression, Spectral Features, Semantic Features, Hindi Audio
The speech emotion recognition system is the aim of our paper to emotion from Hindi audio. So we extract audio as well as text based character from input audio speech to detect emotions. ML methods like Random Forest, Logistic Regression used to both audio and text datasets separately. This combined outcome can be utilized to find four emotions namely neutral, angry, sad and happy.
- EmoMatchSpanishDB: study of speech emotion recognition machine learning models in a new Spanish elicited database
Keywords:
Affective analysis, EmoMatchSpanishDB, Language resources
Our paper offers a new speech emotion dataset on Spanish. We include crowd sourced perception technique. To remove noisy data and sample emotions crowd sourcing can be helped. We present two datasets EmoSpanishDB and EmoMatchSpanishDB. First the audios are recorded during crowdsourcing process. At second EmoSpanishDB only audios whose audio match with original. At last, the different state of the art ML methods in terms accuracy, precision and recall for both datasets.
- Speech Emotion Recognition in Machine Learning to Improve Accuracy using Novel Support Vector Machine and Compared with Random Forest Algorithm
Keywords:
Novel SVM algorithm, Speech Emotion, .wav audio, Feature Extraction, Supervised Learning
To examine human behaviour and predict human emotion by utilizing ML method of SVM and RF methods. There are two groups in our work the first is using SVM method and the second is using RF method. Thr SVM performs better than RF.
- Recognizing Speech Emotions in Iraqi Dialect Using Machine Learning Techniques
Keywords
Speech emotions, Iraqi Dialect
Our paper uses ANN based speech emotion recognition (SER) is suggested to detect three emotions for speakers speaking in Iraqi dialect, employing Mel-frequency cepstral coefficients (MFCC) as essential characters. There are no benchmark datasets for Iraqi SER and the speech of some Iraqi people of both genders is recorded.
- The Emotion Probe: On the Universality of Cross-Linguistic and Cross-Gender Speech Emotion Recognition via Machine Learning
Keywords:
Artificial intelligence; English; cross-linguistic; cross-gender; SVM; SER
Our paper discovers the feature of cross-linguistic, cross-gender SER and three ML classifiers were used (SVM, Naïve Bayes and MLP) and get steps based on Kononenko’s discretization and correlation-based feature selection. We used five emotions namely disgust, fear, happiness, anger and sadness. Thr MLP shows the better outcome. RASTA, F0, MFCC and spectral energy are the four feature domains most effective and the method based on standard sets.
- Machine Learning Applied to Speech Emotion Analysis for Depression Recognition
Keywords:
Support Vector Machine, Depression
Our paper helps clinical management during therapy as well as early detection of depression. To detect different emotion a new computational method can be used. The two data set for audio can be used namely DAIC-WOZ and RAVDESS dataset for depression related data. Finally LSTM performance is compared with SVM.
- IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients
Keywords:
IoT WBAN; edge AI; speech emotion; CNN; BiLSTM; standard scaler; min–max scaler; robust scaler; data augmentation; spectrograms; regularization techniques; MFCC; Mel spectrogram
IoT-based wireless body area network (WBAN) is used for healthcare management.Our paper uses a hybrid DL method ie. CNN and bidirectional LSTM and a regularized CNN model. We combine this with various optimization techniques and regularization method to improve prediction accuracy, reduce error and computational complexity. The metrics to evaluate are prediction accuracy, precision, recall, F1 score and confusion matrix.
- Emotion Recognition in Arabic Speech From Saudi Dialect Corpus Using Machine Learning and Deep Learning Algorithms
Keywords:
Arabic speech, Saudi dialect, KNN
Our paper examines the emotion recognition system in Arabic and the database was taken from YouTube channel. Four emotions such as happiness, sad and neutral. we extract features from audio signals such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), and also we used SVM, KNN and DL methods as CNN and LSTM.
- Automatic Speech Emotion Recognition Using Machine Learning: Mental Health Use Case
Keywords:
Mental health, tele-mental health, speech analysis, automatic emotion recognition
In this paper we can automatic-speech-emotion-recognition for mental health purposes. Our paper uses five machine learning methods to classify emotions and calculate their performance by concentrate human emotion by benchmark datasets such as TESS, EMO-DB, and RAVDESS established better performance.