Speech Technology with Tencent AI Lab’s AutoPrep for Optimal Unstructured Speech Data Processing
Tencent AI Lab has pioneered a groundbreaking preprocessing framework, AutoPrep, dedicated to refining unstructured, in-the-wild speech data, poised to redefine speech data processing standards by delivering automated preprocessing and elite annotation for such data. AutoPrep confronts the prevalent challenges in speech technology, such as lack of quality annotations and the inherent limitations in existing datasets, providing a well-rounded solution that not only elevates speech quality and automates speaker labels but also ensures precise transcriptions. It includes six core components; speech enhancement, segmentation, speaker clustering, target speech extraction, quality filtering, and automatic speech recognition, collectively transforming raw speech data into premium, annotated data conducive for diverse speech technology applications. The framework has demonstrated its efficacy and reliability through experiments on open-sourced corpora, proving its merit in various tasks like Text-to-Speech (TTS), Speaker Verification (SV), and Automatic Speech Recognition (ASR) model training.