Syllable Tokenization

Syllable tokenization, the process of dividing speech or text into syllables, is gaining traction in natural language processing (NLP) and speech recognition. Current research focuses on applying syllable-based models to improve the performance of language models, particularly for low-resource languages and speech-to-text systems, often employing techniques like deep neural networks (DNNs) and hidden Markov models (HMMs). This approach offers advantages in handling linguistic variations within and across languages, leading to more accurate and efficient processing, with applications ranging from improved language models for diverse languages to real-time speech emotion recognition.