Enhancing Human Emotion Detection in Audio Data with Deep Neural Networks Using Cross-Dataset

Understanding emotions from spoken language is a natural human ability, but it brings machines into great perplexity due to the complex variation in an individual’s speech. Our research focuses on working out this challenge by developing a model that makes use of deep neural networks, particularly Convolutional Neural Networks(CNN), for the recognition of emotions from speech. The goal is to be equipped with a system capable of independent emotion detection for a variety of audio datasets, adaptation to new situations, and staying accurate even in fully new cases of data. To achieve this, we combined two strong datasets: the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS). The TESS dataset is particularly useful because it includes high-quality recordings from female speakers, helping to balance the gender differences often found in other datasets. This balance makes our model better at recognizing emotions across different voices. We implemented state-of-the-art techniques in audio feature extraction so that the input to our model only consists of relevant features, enabling it to extract the emotional content correctly. Our model was trained on eight emotions: neutral, calm, happy, sad, angry, fearful, disgusted, and surprised. For measuring model performance, we used the F1 score and obtained a weighted average of 0.94 on the test set. It did best on classifying the "Calm" emotion, with an accuracy of 0.97, and worst for the "Sad" emotion, with an accuracy of 0.91. Notwithstanding, our model outperforms current models in handling the huge diversity of voices in speech data.