Speech recognition systems process filler words like "um" and "uh" through a combination of language modeling, acoustic modeling, and contextual understanding. Generally, these systems are designed to recognize spoken language as accurately as possible, focusing on converting what was said into text. Filler words are often seen as non-essential to the overall meaning of the spoken content. However, ignoring them altogether would lead to less natural transcriptions, as these sounds are common in everyday conversation.
To handle filler words, acoustic models are trained to recognize various phonetic sounds, including those made by "um" and "uh". During the training phase, the models listen to numerous examples of speech, learning to identify the acoustic signatures of different sounds. Based on this training, when a speech recognition system processes audio input, it assigns probability scores to various sounds, which helps it detect and transcribe filler words as well as other phonetic components. For example, the system may encounter a phrase like "I, um, think we should go" and recognize the filler "um" in the context of the surrounding speech.
In practice, developers have options regarding how their applications handle these filler words. Some systems may choose to include them in the final text, maintaining a more realistic representation of speech, while others might omit them to produce a cleaner output. Developers can adjust the algorithms and models to lean toward either approach based on the requirements of the application, such as whether it is for real-time transcription services, virtual assistants, or closed captioning. Understanding this process allows developers to make informed choices about implementing speech recognition in their projects.