Dictionary Learning: Performance through Consistency

We present first results from our efforts 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 modifications 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..

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]

Testing Generality In Janus: A Multi-Lingual Speech Translation System

For speech translation to be practical and useful, speech translation systems should be portable to multiple languages without substantial modification. We present the results of expanding the English-based JANUS speech translation system [1] to translate from spoken German sentences to English and Japanese utterances. We also report the results of implementing part of the LPNN speech recognition module on a massively parallel machine. The JANUS approach generalizes well, with overall system performance of 97%. This surpasses English-based JANUS performance