Auto Spell Suggestion for High Quality Speech Synthesis in Hindi
This addresses the issue of low-quality speech synthesis for Hindi users due to spelling confusion, but it is incremental as it applies an existing spellchecking method to a specific language domain.
The paper tackles the problem of input text errors degrading Hindi speech synthesis quality by integrating an automatic spellchecker to suggest corrections for misspelled words, resulting in improved efficiency and phonetic text quality as shown in a comparative study.
The goal of Text-to-Speech (TTS) synthesis in a particular language is to convert arbitrary input text to intelligible and natural sounding speech. However, for a particular language like Hindi, which is a highly confusing language (due to very close spellings), it is not an easy task to identify errors/mistakes in input text and an incorrect text degrade the quality of output speech hence this paper is a contribution to the development of high quality speech synthesis with the involvement of Spellchecker which generates spell suggestions for misspelled words automatically. Involvement of spellchecker would increase the efficiency of speech synthesis by providing spell suggestions for incorrect input text. Furthermore, we have provided the comparative study for evaluating the resultant effect on to phonetic text by adding spellchecker on to input text.