We present first results from our efforts in automatically increasing and adapting phonetic dictionaries for sponta neous speech recognition. Spontaneous speech adds a vari ety of phenomena to a speech recognition task: false starts [1], human and nonhuman noises [2] , new words [3] and al t.ernative 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 pro nunciations of a word according to the frequency in which they appear in the database rather than the "correct" pro nunciation 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 ap proach can lead to significant 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 spon taneous speech-to-speech translation system of the Inter active Systems Laboratories at Carnegie Mellon and Karl sruhe University. The phonetic dictionary is one of the main knowledge sources for a speech recognizer, to lead it to valid hypotheses in the recognition process. Still it is often regarded as being less important as acoustic or language modeling. In continuous speech recognizers researchers often use the "correct" pronunciation of a word, as it can be found in a lexicon. But 1.11is "correct" prouunciation doesn't have to be the most frequent variant for a given task (e.g. in spon taneous speech), nor does it necessarily yield the best recog nition performance, given the· current acoustic modeling. If the phonetic transcription� in the dictionary don't match the actual occurrences in the database, the phonetic units will be contaminated during the training with inadequate acoustics. This will degrade the overall performance of the recognizer. • with increasing number of basic phonetic units (usu ally between 40 and 100) and number of entries in the dictionary, it gets more and more difficult to use the phonetic units consistently across dictionary entries. • experts tend to use the "correct" phonetic transcription of a word (as it could be found in al cxieon) -this isn't necessarily the most frequent or even the most likely transcription for a given task. • alternative pronunciations can be very different from the "correct" pronunciation. In spontaneous speech and in dialects a lot of alternative pronunciations are used which are not always easy to predict. • as it is hard to say which variants are statistically rele vant for a given task, the maintainer of the dictionary can easily miss a relevant form. Therefore we propose a data-driven approach to improve existing dictionaries and automatica.lJ y add new variants whenever needed. This algorithm should: • nse a performance driven optimization of the phonetic entries in the dictionary rather than a "canonical" form of a word. • use the underlying phonetic modeling to generate accu rate and consistent entries in the phonetic dictionary. • generate pronunciation variants, only if they are sta. tistica.lJy relevant. • lead to a lower phoneme confusability after retraining. In our experiments we showed that even using a simple algorithm to extract candidates for phonetic variants yields a. significant increas e in recognition performance. More so phisticated algorithms yield even better performances. 0-7803-2431-5/95 $4.00 © 1995 IEEE
REF uses the first criterion: the selection of the k most frequently extracted phonetic transcriptions.
Dictionary learning: performance through consistency
1995 International Conference on Acoustics, Speech, and Signal Processing
1995 International Conference on Acoustics, Speech, and Signal Processing
Computer Science