Non-Standard Vietnamese Word Detection and Normalization for Text-to-Speech

Converting written texts into their spoken forms is an essential problem in any text-to-speech (TTS) systems. However, building an effective text normalization solution for a real-world TTS system face two main challenges: (1) the semantic ambiguity of non-standard words (NSWs), e.g., numbers, dates, ranges, scores, abbreviations, and (2) transforming NSWs into pronounceable syllables, such as URL, email address, hashtag, and contact name. In this paper, we propose a new two-phase normalization approach to deal with these challenges. First, a model-based tagger is designed to detect NSWs. Then, depending on NSW types, a rule-based normalizer expands those NSWs into their final verbal forms. We conducted three empirical experiments for NSW detection using Conditional Random Fields (CRFs), BiLSTM-CNN-CRF, and BERT-BiGRU-CRF models on a manually annotated dataset including 5819 sentences extracted from Vietnamese news articles. In the second phase, we propose a forward lexicon-based maximum matching algorithm to split down the hashtag, email, URL, and contact name. The experimental results of the tagging phase show that the average F1 scores of the BiLSTM-CNN-CRF and CRF models are above 90.00%, reaching the highest F1 of 95.00% with the BERT-BiGRU-CRF model. Overall, our approach has low sentence error rates, at 8.15% with CRF and 7.11% with BiLSTM-CNN-CRF taggers, and only 6.67% with BERT-BiGRU-CRF tagger.Comment: The 14th International Conference on Knowledge and Systems Engineering (KSE 2022

Rule based learning of word pronunciations from training corpora

Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (leaves 83-85).This paper describes a text-to-pronunciation system using transformation-based error-driven learning for speech-recognition purposes. Efforts have been made to make the system language independent, automatic, robust and able to generate multiple pronunciations. The learner proposes initial pronunciations for the words and finds transformations that bring the pronunciations closer to the correct pronunciations. The pronunciation generator works by applying the transformations to a similar initial pronunciation. A dynamic aligner is used for the necessary alignment of phonemes and graphemes. The pronunciations are scored using a weighed string edit distance. Optimizations were made to make the learner and the rule applier fast. The system achieves 73.9% exact word accuracy with multiple pronunciations, 82.3% word accuracy with one correct pronunciation, and 95.3% phoneme accuracy for English words. For proper names, it achieves 50.5% exact word accuracy, 69.2% word accuracy, and 92.0% phoneme accuracy, which outperforms the compared neural network approach.Lajos Molnár.M.Eng.and S.B

Statistical morphological disambiguation with application to disambiguation of pronunciations in Turkish /

The statistical morphological disambiguation of agglutinative languages suffers from data sparseness. In this study, we introduce the notion of distinguishing tag sets (DTS) to overcome the problem. The morphological analyses of words are modeled with DTS and the root major part-of-speech tags. The disambiguator based on the introduced representations performs the statistical morphological disambiguation of Turkish with a recall of as high as 95.69 percent. In text-to-speech systems and in developing transcriptions for acoustic speech data, the problem occurs in disambiguating the pronunciation of a token in context, so that the correct pronunciation can be produced or the transcription uses the correct set of phonemes. We apply the morphological disambiguator to this problem of pronunciation disambiguation and achieve 99.54 percent recall with 97.95 percent precision. Most text-to-speech systems perform phrase level accentuation based on content word/function word distinction. This approach seems easy and adequate for some right headed languages such as English but is not suitable for languages such as Turkish. We then use a a heuristic approach to mark up the phrase boundaries based on dependency parsing on a basis of phrase level accentuation for Turkish TTS synthesizers