Speech Emotion Recognition using Python

Speech is simply the most common method for communicating as people. It is just common at that point only natural then to extend out this correspondence medium to PC applications. We characterize speech emotion recognition (SER) as an assortment of systems that procedure and classify speech signals to detect the embedded emotions. In simple words, It is the act of attempting to recognize human emotion and affective states from speech. This is the system that will significantly take a shot at the way that voice frequently reflects hidden feelings through tone and pitch. SER is tough because emotions are subjective and annotating audio is challenging. By using this system, we can identify the human emotion like sad, cheerful, calm, angry, happy, fearful, regret, etc. by their speech or voice or we can say using some audio.

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Project Description-

In this project, I will utilize the libraries' librosa, sound record, and sklearn to construct a model utilizing an MLPClassifier. This will have the option to perceive feelings from sound documents. At that point, we'll instate an MLPClassifier and train the model. Let start-

1. This project requires some information on points like Python, sklearn, librosa, and so on.
2. Python-Python is easy to learn and work on with the language. It is an elevated level, broadly useful programming and profoundly intruded on language.
3. Librosa -Librosa is a Python library for examining sound and music. It has a compliment bundle format, institutionalized interfaces, and names, in reverse similarity, measured capacities, and meaningful/readable code.
4. Point to be noted, “Programmers can explore different avenues regarding the programming language as demonstrated by their comfort level and data" and can change the recently referenced language according to them.

Project Implementation-

1. To start with, install the necessary packages for the project
2. Now, Download the dataset from the internet and also make necessary imports like librosa, sound file, etc.
3. It is time to get in the coding part, first define a function extract_feature to extract the mfcc, chroma, and mel features from a sound file.
4. Next, we need to define a dictionary to hold numbers and the feelings accessible in the dataset, and a rundown to hold those we want- sad, cheerful, calm, angry, happy, fearful, regret, etc.
5. Now, let’s load the data with a function load_data().
6. After loading the data, now extract the feature and split the dataset into training and testing sets and watch the state of the preparation of the training and testing datasets.
7. Now, get the number of features extracted.
8. Lastly, let’s initialize an MLPClassifier.
9. Now train the model.
10. The model is ready, run and watch the result.
11. We can also check the accuracy of the model using accuracy function().

Software Requirement -

-Programming language -  Python

-Operating System - any os like a window, ubuntu.

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Speech Emotion Recognition using Python