Speech Emotion Detection System using Python

Speech is a natural and intuitive way to communicate and express emotions. As speech and language processing technologies have advanced, speech emotion detection has become an increasingly important area of research. Speech emotion detection systems can be used in various applications, such as in-call centers, mental health counseling, and social robotics.

In this blog post, we will explore how to build a simple speech emotion detection system using Python. We will use the open-source library called librosa to extract features from speech signals and then use machine learning techniques to classify the emotion.

First, we need to install the required libraries:

!pip install librosa
!pip install soundfile
!pip install numpy
!pip install sklearn

Next, we need to import the necessary libraries:

import librosa
import soundfile
import numpy as np
import sklearn
import os

We will use the RAVDESS dataset, a publicly available dataset of speech recordings that includes eight emotions: neutral, calm, happy, sad, angry, fearful, disgusted, and surprised. We will use the first two seconds of each speech recording and extract the Mel-frequency cepstral coefficients (MFCCs) as features.

emotions = {
    '01': 'neutral',
    '02'…