Dictionary learning: performance through consistency

We present rst results from our e orts in automatically increasing and adapting phonetic dictionaries for spontaneous speech recognition. Spontaneous speech adds a variety of phenomena to a speech recognition task: false starts [1], human and nonhuman noises [2], new words [3] and alternative pronunciations. All of these phenomena have to be tackled when adapting a speech recognition system for spontaneous speech. For phonetic dictionaries (especially for spontaneous speech) it is important to choose the pronunciations of a word according to the frequency in which they appear in the database rather than the \correct" pronunciation as it might be found in a lexicon. Additionally modi cations of the dictionary should not lead to a higher phoneme confusability. Therefore we propose a data-driven approach to add new pronunciations to a given phonetic dictionary, in a way that they model the given occurrences of words in the database. We show how even a simple approach can lead to signi cant improvements in recognition performance. First experiments have been performed on the German Spontaneous Scheduling Task (GSST), using the speech recognition engine of JANUS-2 [4, 5, 6], the spontaneous speech-to-speech translation system of the Interactive Systems Laboratories at Carnegie Mellon and Karlsruhe University.