Automatic generation of multiple pronunciations based on neural networks
Speech Communication
In search of better pronunciation models for speech recognition
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Maximum likelihood modelling of pronunciation variation
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Stochastic pronunciation modelling from hand-labelled phonetic corpora
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Modeling pronunciation variation for ASR: a survey of the literature
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Dynamic pronunciation models for automatic speech recognition
Dynamic pronunciation models for automatic speech recognition
Automatic phonetic transcription of large speech corpora
Computer Speech and Language
On the utility of syllable-based acoustic models for pronunciation variation modelling
EURASIP Journal on Audio, Speech, and Music Processing
Multiword expressions in spoken language: An exploratory study on pronunciation variation
Computer Speech and Language
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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.