Speech emotion recognition is a practice of integrating acoustic characteristic of a spoken speech so as to identify and label the emotional disposition of the speaker. In the present paper, an end-to-end architecture of the speech emotion recognition is going to be presented using the RAVDESS data set. This methodological integration includes natural features extraction, high quality of audio processing, smart data augmentation, and deep learning that enabled the successful categorization of emotional speech signals. In this paper, the descriptions of the arrangements performed on its RAVDESS dataset to arrange it by actor in actor-specific subdirectories and the cautious readiness of the audio files are presented as the basis of the paper. The key characteristics are obtained through the assistance of Librosa library; they are provided to be zero-crossing rate, root mean square energy, Mel spectrograms, and Mel-frequency cepstral coefficients (MFCCs). The results show that the combination of the traditional signal processing approaches and the modern neural networks architecture, like Long Short Term Memory (LSTM) based models, contributes to the high accuracy speech emotion recognition.