G2P variant prediction techniques for ASR and STD

Abstract

Introducing pronunciation variants into a lexicon is a balancing act: incorporating necessary variants can improve automatic speech recognition (ASR) and spoken term detection (STD) performance by capturing some of the variability that occurs naturally; introducing superfluous variants can lead to increased confusability and a decrease in performance. We experiment with two very different grapheme-to-phoneme variant prediction techniques and analyze the variants generated, as well as their effect when used within fairly standard ASR and STD systems with unweighted lexicons. Specifically, we compare the variants generated by joint sequence models, which use probabilistic information to generate as many or as few variants as required, with a more discrete approach: the use of pseudophonemes within the default-and-refine algorithm. We evaluate results using three of the 2013 Babel evaluation languages with quite different variant characteristics – Tagalog, Pashto and Turkish – and find that there are clear trends in how the number and type of variants influence performance, and that the implications for lexicon creation for ASR and STD are different. Index Terms: pronunciation variants, speech recognition, spoken term detection, grapheme-to-phoneme