Speech Emotion Recognition using Machine Learning Project

Machine Learning (ML) based Speech Emotion Recognition (SER) is considered as an integrative domain merging ML methods and signal processing to detect and categorize emotions by analyzing speech signals. It is an interesting approach and has a wide range from human-computer communication to mental health tracking. Selecting topic for machine learning is a crucial step by we are here to select the suitable topic from international journal that matches with your interest get non-plagiarized research paper writing from phddirection.com for speech emotion recognition by trending methodologies.

Here, we discuss about procedural steps to develop this research topic:

  1. Objective Description:

            Ensure whether our goal is to recognize happy, angry, neutral or sad emotions and determine whether we focus on categorical classification or consistent regression (for instance: forecasting arousal and valence values).

  1. Data Gathering:
  • Public Datasets: Our research utilizes various datasets such as TESS, Emo-DB, RAVDESS and CREMA-D and these datasets are full of actors’ voice recording data expressing several emotions.
  • Own Collection: Make sure whether we consider moral suggestions and have legal rights when we intend to gather our own data.
  1. Feature Extraction:

From the audio signals, we extract various essential features such as:

  • Frequency Domain Features: Our approach extracts spectral bandwidth, spectral contrast, Mel Frequency Cepstral Coefficients (MFCCs), etc.
  • Higher-Level Features: We consider Chroma features, Formants, Tonnetz, etc.
  • Time Domain Features: Some of the features we retrieve are Variance, Mean, skewness and kurtosis of the waveform.
  • Prosodic Features: Features such as Pitch, energy and speaking rate help us to carry out this project.
  1. Preprocessing of Data:
  • Segmentation: For analysis purposes, we split the consistent speech signal into tiny parts.
  • Standardization or Normalization: Our work maintains the same dimension among all features.
  • Data Augmentation: By utilizing random transformations (for instance: pitch-shifting, time-stretching) to audio signals, we widen our training data.
  1. Model Chosen & Training:
  • Conventional ML Techniques: Our work employs Random Forest, gradient Boosting Machines, Decision Trees and Support Vector Machines.
  • Deep Learning: Some of the methods achieve better outcomes in our SER framework and they are CNNs, integrated architectures and RNNs.
  • Recurrent Neural Networks: Specifically we utilize GRU or LSTM for Sequences based audio signals.
  • Model Ensemble: To enhance overall accuracy, our approach integrates forecasting from various frameworks.
  1. Evaluation:
  • Metrics: We utilize various metrics like mean squared error, accuracy, F1-score, etc based on the tasks such as categorization or regression.
  • Confusion Matrix: A confusion matrix assists us to interpret where our framework makes errors in categorization tasks.
  1. Deployment:

In various actual-world platforms, we implement our trained framework:

  • Actual-time Monitoring: To track user satisfaction, implement our framework in call centers.
  • Healthcare: To detect the indication of anxiety or stress, we monitor patients.
  • Human-Computer Communication: Based on an individual’s emotional mind set, we enable the computer or devices to be more interactive.

Future Improvements:

  • Multimodal Emotion Recognition: To enhance accuracy, we integrate other approaches such as facial features with speech data.
  • Transfer Learning: Our project utilizes pre-trained frameworks on a huge dataset and adjusts them for our particular dataset.
  • Actual-time SER: It is very difficult to perform feature extraction and forecasting processes when we intend to deploy our framework to work in an actual-time platform.


  • Variability: We find that, on the basis of cultures, one’s character and gender, emotions may be conveyed variously.
  • Ambiguity: Often it is very complicated for us to detect emotions by analyzing speech.
  • Data Confidentiality: Specifically when we capture or utilize human’s speech, sure about maintaining the confidentiality of the user.

Check whether we have proper interpretation of both moral and technical factors of this specified domain. It is very important to manage the confidentiality of users and be clear about how we utilize capturing and forecasting when the SER model becomes robust. As we have valuable years of experience our professionals write Survey paper on university standards within the deadline and in high quality.

Speech Emotion Recognition using Machine Learning Ideas

Speech Emotion Recognition Using Machine Learning Project Thesis Ideas

With our 18+ research experience we propose unique thesis topics where you can score higher rank in your academics’ we carry out the research and reach out the solutions correctly. Thesis proposals will be properly written by our team of ML experts. Customized thesis topics are selected as per your choice and we hence carry the research work.

Some of our research works are stated below.

  1. Speech emotion recognition for psychotherapy: an analysis of traditional machine learning and deep learning techniques


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. 

  1. Machine Learning based Speech Emotion Recognition in Hindi Audio


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.

  1. EmoMatchSpanishDB: study of speech emotion recognition machine learning models in a new Spanish elicited database


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 dataset.

  1. Speech Emotion Recognition in Machine Learning to Improve Accuracy using Novel Support Vector Machine and Compared with Random Forest Algorithm


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.

  1. Recognizing Speech Emotions in Iraqi Dialect Using Machine Learning Techniques


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.

  1. The Emotion Probe: On the Universality of Cross-Linguistic and Cross-Gender Speech Emotion Recognition via Machine Learning


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.  

  1. Machine Learning Applied to Speech Emotion Analysis for Depression Recognition


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.

  1. IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients


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. 

  1. Emotion Recognition in Arabic Speech From Saudi Dialect Corpus Using Machine Learning and Deep Learning Algorithms


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.

  1. Automatic Speech Emotion Recognition Using Machine Learning: Mental Health Use Case


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.

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