Dictionary learning for spontaneous speech recognition

Spontaneous speech adds a variety of phenomena to a speech recognition task: false starts, human and nonhuman noises, new words, and alternative pronunciations. All of these phenomena have to be tackled when adapting a speech recognition system for spontaneous speech. In this paper we will focus on how to automatically expand and adapt phonetic dictionaries for spontaneous speech recognition. 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 might be found in a lexicon. Therefore, we proposed a data-driven approach to add new pronunciations to a given phonetic dictionary [1] in a way that they model the given occurrences of words in the database. We will show how this algorithm can be extended to produce alternative pronunciations for word tuples and frequently misrecognized words. We will also discuss how further knowledge can be incorporated into the phoneme recognizer in a way that it learns to generalize from pronunciations which were found previously. The experiments have been performed on the German Spontaneous Scheduling Task (GSST), using the speech recognition engine of JANUS 2, the spontaneous speech-to-speech translation system of the Interactive Systems Laboratories at Carnegie Mellon and Karlsruhe University [2, 3].