Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion. In Human Language Technologies 2007: The Conference of the North ...
This work presents a novel technique of training with many-to-many alignments of letters and phonemes, and applies an HMM method in conjunction with a local ...
Letter-to-phoneme conversion generally requires aligned training data of letters and phonemes. Typically, the align- ments are limited to one-to-one align- ...
Letter-to-phoneme conversion generally requires aligned training data of letters and phonemes. Typically, the alignments are limited to one-to-one alignments.
Missing: Hidden Markov
This algorithm creates lexicon alignments without requiring annotated data nor linguistic knowledge. Its principle algorithm is based on the Ristad and ...
Phonetization is a crucial step for oral document processing. In this paper, a new letter-to-phoneme conversion approach is pro- posed; it is automatic, ...
Letter-to-Sound(LTS) conversion, which is used to compress the lexicon for embedded application purpose, has become an important part in Text-to-Speech ...
Applying many-to-many alignments and hidden markov models to letter-to-phoneme conversion. In Human Language Technologies 2007: The Conference of the North ...
This work presents a novel technique of training with many-to-many alignments of letters and phonemes, and applies an HMM method in conjunction with a local ...
The first-stage neural network is fundamentally implemented as a many-to-many mapping model for automatic conversion of word to phoneme sequences, while the ...