A dynamic approach to paradigm-driven analogy

The general point is made that analogy-based self-learning techniques are no longer just psycholinguistically-plausible models, but competitive tools, combining the advantages of using language-independent, self- learning, tractable algorithms, with the welcome bonus of being more reliable for applications than traditional text-to-speech systems.

Abstract

When looked at from a multilingual perspective, grapheme-to-phoneme conversion is a challenging task, fraught with most of the classical NLP "vexed questions": bottle-neck problem of data acquisition, pervasiveness of exceptions, difficulty to state range and order of rule application, proper treatment of context-sensitive phenomena and long-distance dependencies, and so on. The hand-crafting of transcription rules by a human expert is onerous and time-consuming, and yet, for some European languages, still stops short of a level of correctness and accuracy acceptable for practical applications. We illustrate here a self-learning multilingual system for analogy-based pronunciation which was tested on Italian, English and French, and whose performances are assessed against the output of both statistically and rule-based transcribers. The general point is made that analogy-based self-learning techniques are no longer just psycholinguistically-plausible models, but competitive tools, combining the advantages of using language-independent, self-learning, tractable algorithms, with the welcome bonus of being more reliable for applications than traditional text-to-speech systems.