Level Acoustic Information

Level acoustic information processing in audio analysis focuses on effectively extracting and utilizing diverse acoustic features from speech and other audio signals to improve performance in tasks like speech emotion recognition and depression detection. Current research emphasizes the integration of multi-level acoustic features, often employing hierarchical attention mechanisms, transformer architectures (like MAST), and multimodal fusion techniques to combine low-level spectrograms with higher-level representations derived from models such as wav2vec2. These advancements are driving improvements in the accuracy and robustness of AI systems for applications ranging from human-computer interaction to mental health diagnostics.