Editing Multimodal Speech Recognition (section)

=== Multimodal Speech Recognition's Influence in Real Life ===
Firstly, multimodal speech recognition has promoted the development of natural human-computer interaction techniques. Users can engage with computing systems and devices in a more intuitive manner, combining speech, gestural inputs, and facial expressions. For example, Users can interact and explore virtual worlds immersive through voice and gestures.

In the healthcare sector, multimodal speech recognition has been useful in promoting communication between doctors and patients. This technology facilitates real-time transcription of spoken content and contributes to the smooth collecting of medical diagnoses and patient records. It means that the system can recognize the special place in the body where patients point by their fingers, combining speech content to summarize more accurate and time-saving medical diagnoses.

Also, the integration of multimodal speech recognition into smart home ecosystems and Internet of Things (IoT) devices has introduced significant change. Users can exercise control over household appliances such as [[Introduction of Voice Assistants|voice assistant]], lighting systems, and smart locks via spoken commands and other sensory modalities. Such as the smart lock can have a double password, gesture with voice, combined with a specific combination to open the door. As a result, the security of users can be better guaranteed.

Multimodal speech recognition is primarily driven by the pursuit of enhanced robustness. When dependent on audio signals, speech recognition systems are easily influenced by background noise, speaker's different articulation, and other uncertainties. The integration of insights from diverse modalities enhance these systems to deal with these challenges more effectively. Without it, some instructions made by users to the machine cannot be executed in a noisy environment.