What characterizes the Morphonemic approach in Text to Speech?

The Morphonemic approach in Text to Speech (TTS) systems is characterized by its use of stored morphs and letter-to-sound rules. This approach focuses on the relationship between letters and their corresponding sounds, particularly at the level of morphemes, which are the smallest meaningful units of language. By employing these stored morphs, the system can accurately convert written language into spoken output, capturing the nuances of language that come from the morphological structure. This method allows for a more flexible and context-aware pronunciation that can adapt based on the morphological composition of words, leading to more natural-sounding speech.

This stands in contrast to other approaches that may rely more heavily on strict phonetic transcription, auditory feedback, or digital signal processing. Phonetic transcription focuses primarily on the sounds rather than the morphological structure, while auditory feedback emphasizes user interaction rather than the underlying linguistic rules. Digital signal processing is a broader field that deals with the manipulation of sound signals but does not specifically address the morphological aspects of TTS.