Final answer:
A speech recognition engine uses machine learning algorithms and natural language processing to convert speech into text, not optical character recognition or audio compression. Encoding verbal information is most effective through semantic processing, according to the research done by Craik and Tulving.
Step-by-step explanation:
The speech recognition engine turns speech into text primarily using machine learning algorithms and natural language processing (NLP). While optical character recognition is used for converting images of text into machine-encoded text, it does not apply to audio. Similarly, audio compression reduces the size of audio files but is not used for converting speech to text. On the other hand, machine learning algorithms are employed to interpret the audio waves, and natural language processing is used to understand the context and semantics of the spoken words to generate accurate text representations.
In a psychological context, as investigated by Fergus Craik and Endel Tulving, encoding verbal information is best done through semantic processing. Their experiments found that processing the words for meaning (semantic) led to better recall or recognition compared to just processing for visual or acoustic characteristics.