Using morphology towards better large-vocabulary speech recognition systems

To guarantee unrestricted natural language processing, state-of-the-art speech recognition systems require huge dictionaries that increase search space and result in performance degradations. This is especially true for languages where there do exist a large number of inflections and compound words such as German, Spanish, etc. One way to keep up decent recognition results with increasing vocabulary is the use of other base units than simply words. Different decomposition methods originally based on morphological decomposition for the German language are compared. Not only do they counteract the immense vocabulary growth with an increasing amount of training data, also the rate of out-of-vocabulary words, which worsens recognition performance significantly in German, is decreased. A smaller dictionary also leads to 30 K speed improvement during the recognition process. Moreover even if the amount of available training data is quite huge it is often not enough to guarantee robust language model estimations, whereas morphem-based models are capable to do so.