Normalization of Lithuanian Text Using Regular Expressions
원문 초록
Text Normalization is an integral part of any text-to-speech synthesis system. In a natural language text, there are elements such as numbers, dates, abbreviations, etc. that belong to other semiotic classes. They are called non-standard words (NSW) and need to be expanded into ordinary words. For this purpose, it is necessary to identify the semiotic class of each NSW. The taxonomy of semiotic classes adapted to the Lithuanian language is presented in the work. Sets of rules are created for detecting and expanding NSWs based on regular expressions. Experiments with three completely different data sets were performed and the accuracy was assessed. Causes of errors are explained and recommendations are given for the development of text normalization rules.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching
Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models
MoonCast: High-Quality Zero-Shot Podcast Generation
Multimodal Latent Language Modeling with Next-Token Diffusion
Lina-Speech: Gated Linear Attention is a Fast and Parameter-Efficient Learner for text-to-speech synthesis
S2ST-Omni: An Efficient and Scalable Multilingual Speech-to-Speech Translation Framework via Seamless Speech-Text Alignment and Streaming Speech Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.