On the use of discriminative and non-discriminative pronunciation priors in pronunciation variation modeling of non-native proper names

The large amount of variation present in native speakers' pronunciation of non-native proper names is a big challenge for most automatic speech recognition systems today. The recognizer's ability to handle a variety of different pronunciations is therefore critical to achieve an acceptable recognition performance for this task. This problem has traditionally been solved by including alternative pronunciation variants in the recognition lexicon. To reduce the unwanted confusion that this might introduce between different lexicon entries, several studies have tried incorporating pronunciation prior probabilities into the recognition process. In this paper, we compare three different approaches for training these pronunciation prior probabilities based on: probabilities learned during the pronunciation variant generation and the discriminative frameworks Maximum Entropy and Minimum Classification Error. The recognition results obtained using these prior probabilities as a variant selection criterion are evaluated and a comparative error analysis is performed.