NeMo Inverse Text Normalization: From Development To Production
This work addresses the need for reliable ITN systems in ASR applications, but it is incremental as it builds on existing WFST-based methods with a new library.
The paper tackles the problem of converting spoken-domain automatic speech recognition (ASR) output into written-domain text to improve readability, introducing an open-source Python WFST-based library for inverse text normalization (ITN) that enables a seamless path from development to production.
Inverse text normalization (ITN) converts spoken-domain automatic speech recognition (ASR) output into written-domain text to improve the readability of the ASR output. Many state-of-the-art ITN systems use hand-written weighted finite-state transducer(WFST) grammars since this task has extremely low tolerance to unrecoverable errors. We introduce an open-source Python WFST-based library for ITN which enables a seamless path from development to production. We describe the specification of ITN grammar rules for English, but the library can be adapted for other languages. It can also be used for written-to-spoken text normalization. We evaluate the NeMo ITN library using a modified version of the Google Text normalization dataset.