A data-driven method for modeling pronunciation variation
This paper describes a rule-based data-driven (DD) method to model pronunciation variation in automatic speech recognition (ASR). The DD method consists of the following steps. First, the possible pronunciation variants are generated by making each phone in the canonical transcription of the word optional. Next, forced recognition is performed in order to determine which variant best matches the acoustic signal. Finally, the rules are derived by aligning the best matching variant with the canonical transcription of the variant. Error analysis is performed in order to gain insight into the process of pronunciation modeling. This analysis shows that although modeling pronunciation variation brings about improvements, deteriorations are also introduced. A strong correlation is found between the number of improvements and deteriorations per rule. This result indicates that it is not possible to improve ASR performance by excluding the rules that cause deteriorations, because these rules also produce a considerable number of improvements. Finally, we compare three different criteria for rule selection. This comparison indicates that the absolute frequency of rule application (Fabs) is the most suitable criterion for rule selection. For the best testing condition, a statistically significant reduction in word error rate (WER) of 1.4% absolutely, or 8% relatively, is found.
data-driven pronunciation variation rule-based speech recognition error analysis rule selection