Editing State-of-the-art (section)

=== Synthesis ===
The articles reviewed collectively contribute to the ASR field, showing a trend towards multimodal data use, context awareness, and noise reduction techniques to address complexities in human speech such as sarcasm and humor. Key observations include the importance of integrating audio, visual, and textual data for better sarcasm detection, the effectiveness of dual-channel noise reduction in vehicular environments, the application of deep learning for respiratory sound classification and speech enhancement in noisy settings, and data augmentation techniques in improving ASR performances for a specific group of speakers. Challenges mentioned across these studies involve data scarcity, handling diverse dialects, and computational demands. Future research directions suggest a focus on improving ASR systems' adaptability across languages, cultures and groups, better managing non-IID and imbalanced data, and enhancing emotional intelligence in speech recognition. These findings indicate ongoing efforts to make ASR technologies more intuitive and effective in complex human-machine interactions.