What does speech recognition technology convert?

Speech recognition technology is designed to convert spoken language into text. This involves analyzing the audio input, recognizing the different sounds, phonemes, and words, and then transcribing them into written form. The technology utilizes various algorithms and models, including machine learning and deep learning techniques, to accurately interpret the nuances of human speech, such as accents, intonations, and contextual meanings.

This process allows for automatic transcription and facilitates applications like voice search, virtual assistants, and voice-to-text software, where users can dictate instead of type. The effectiveness of speech recognition systems depends on the quality of the audio, the systems' training on diverse datasets, and the complexity of the spoken content.

In contrast, the other options do not pertain to the function of speech recognition. For instance, converting text into images is related to graphics and visualization technologies, while transforming written language into spoken language involves text-to-speech systems, which operate differently from speech recognition. Similarly, audio signals into visual data would concern audio analysis or multimedia processing, but does not align with the primary goal of converting spoken language directly to text.