Speech Tokenization

Speech tokenization aims to represent continuous speech signals as discrete units, enabling the application of powerful language modeling techniques to audio data. Current research focuses on developing effective tokenization methods, often employing vector quantization (VQ) and transformer architectures, and evaluating their performance across various downstream tasks like speech recognition and synthesis, using benchmarks to compare different approaches. Improved tokenization methods are crucial for advancing speech language models, leading to more robust and efficient systems for applications ranging from speech-to-text to audio generation.