While new and large speech and language corpora are being developed rapidly, new techniques have to be examined which particularly support properties of both speech and language processing. However, learning is important for the reduction of knowledge acquisition, for automatic system adaptation, and for increasing the system portability for new domains. Different from most previous approaches, in this paper we demonstrate that hybrid connectionist learning techniques can be used for providing a robust flat analysis of faulty spoken language. Processing spoken language is very different from processing written language, and successful techniques for text processing may not be useful for spoken-language processing. Processing spoken language is less constrained, contains more errors and less strict regularities than written language.
Errors occur on all levels of spoken-language processing.
For instance, acoustic errors, repetitions, false starts and repairs are prominent in spontaneously spoken language. Furthermore, incorrectly analyzed words, unforeseen grammatical and semantic constructions occur very often in spoken language. Therefore we cannot expect that existing techniques like context-free tree representations which have been proven to work for written language can simply be transferred to spoken language. However, currently speech recognizers are still far from perfect and produce many word errors so that it is not possible to rely on a perfect sentence hypothesis. However, suboptimal sentence hypotheses have to be analyzed since sometimes such sentence hypotheses are the best possible output produced by a speech recognizer.