Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest.

ABSTRACT: Speech is a direct and rich way of transmitting information and emotions from one point to another. In this study, we aimed to classify different emotions in speech using various audio features and machine learning models. We extracted various types of audio features such as Mel-frequency cepstral coefficients, chromogram, Mel-scale spectrogram, spectral contrast feature, Tonnetz representation and zero-crossing rate. We used a limited dataset of speech emotion recognition (SER) and augmented it with additional audios. In addition, In contrast to many previous studies, we combined all audio files together before conducting our analysis. We compared the performance of two models: one-dimensional convolutional neural network (conv1D) and random forest (RF), with RF-based feature selection. O

SUBMITTER: Rezapour Mashhadi MM 

PROVIDER: S-EPMC10662716 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature