Project

BlitzScribe: Speech Transcription for the Human Speechome Project

Groups

BlitzScribe is a new approach to speech transcription driven by the demands of today's massive multimedia corpora. High-quality annotations are essential for indexing and analyzing many multimedia datasets; in particular, our study of language development for the Human Speechome Project depends on speech transcripts. Unfortunately, automatic speech transcription is inadequate for many natural speech recordings, and traditional approaches to manual transcription are extremely labor intensive and expensive. BlitzScribe uses a semi-automatic approach, combining human and machine efforts to dramatically improve transcription speed. Automatic methods identify and segment speech in dense, multitrack audio recordings, allowing us to build streamlined user interfaces maximizing human productivity. The first version of BlitzScribe is already about 4-6 times faster than existing systems. We are exploring user-interface design, machine-learning and pattern-recognition techniques to build a human-machine collaborative system that will make massive transcription tasks feasible and affordable.

BlitzScribe is a new approach to speech transcription driven by the demands of today's massive multimedia corpora. High-quality annotations are essential for indexing and analyzing many multimedia datasets; in particular, our study of language development for the Human Speechome Project depends on speech transcripts. Unfortunately, automatic speech transcription is inadequate for many natural speech recordings, and traditional approaches to manual transcription are extremely labor intensive and expensive. BlitzScribe uses a semi-automatic approach, combining human and machine efforts to dramatically improve transcription speed. Automatic methods identify and segment speech in dense, multitrack audio recordings, allowing us to build streamlined user interfaces maximizing human productivity. The first version of BlitzScribe is already about 4-6 times faster than existing systems. We are exploring user-interface design, machine-learning and pattern-recognition techniques to build a human-machine collaborative system that will make massive transcription tasks feasible and affordable.