The paper reports on the evaluation of a rule-based technique to model prototypical non-native pronunciation variants on the symbolic transcription level. This technique was developed to explore the possibility of an automatic generation of adapted pronunciation lexicons for different non-native speaker groups. The rule sets, which are currently available for nine language directions, are based...

A lot of work has been done in deriving the pronunciation dictionary automatically from training data. These attempts focussed mainly on maximum likelihood or similar techniques. Due to the complexity and variability of the pronunciation process it is di cult to nd an adequate pronunciation model. The model will deviate from the truth. Hence, the application of maximum likelihood techniques is ...

Automated speech recognition systems work typically with pronunciation dictionary for generating expected phonetic content of particular words in recognized utterance. But the pronunciation can vary in many situations. Besides the cases with more possible pronunciation variants specified manually in the dictionary there are typically many other possible changes in the pronunciation depending on...

Transliteration is the process of writing a word (mainly proper noun) from one language in the alphabet of another language. This process requires mapping the pronunciation of the word from the source language to the closest possible pronunciation in the target language. In this paper we introduce a new semi-supervised transliteration mining method for parallel and comparable corpora. The metho...

We propose a novel method for lexicon expansion using pronunciation variations extracted on the basis of speaker-related deviations in ASR error statistics. Two types of pronunciation variations were extracted: common pronunciation variations found with most speakers, and speaker-related pronunciation variations, identified on the basis of recognition error elements weighted by idf and tf-idf m...

In this work, we define a measure aimed at assessing how well a pronunciation model will function when used as a component of a speech recognition system. This measure, pronunciation entropy, fuses information from both the pronunciation model and the language model. We show how to compute this score by effectively composing the output of a phoneme recognizer with a pronunciation dictionary and...

Modeling pronunciation variants is an important topic for automatic speech recognition. This paper investigates the pronunciation modeling at the lexical level, and presents a detailed modeling of the probabilities of the pronunciation variants. The approach is evaluated on the French ESTER2 corpus, and a significant word error rate reduction is achieved through the use of context and speaking ...