Parsing spoken phrases despite missing words

The authors compare the recognition accuracy obtained in forming sentence hypotheses using island-driven sentence parsers with parsers that hypothesize sentences in left-to-right fashion. Island-driven parsing algorithms are especially valuable in speech recognition systems because they can function more gracefully when not all of the correct words of an utterance were produced by the word hypothesizer. The inputs to both types of parsers consist of a lattice of candidate words, which are identified by their begin and end times, and the quality of the acoustic-phonetic match. Grammatical constraints are expressed by trigram models of sequences of lexical and semantic labels. The authors found that the island-driven parser produces parses with a higher percentage of correct words than the left-to-right parser is all cases considered. When the quality of the input lattices is extremely high, differences in parsing accuracy can be directly attributed to the superior ability of the island-driven parser to handle lattices with missing words. With lower-quality input, the accuracy of both types of parsers degrades, which is due to the creation of garden-path hypotheses and a lack of good words to serve as seeds for island formation