  This paper addresses the problem of correctly formatting numeric expressions
in automatic speech recognition (ASR) transcripts. This is challenging since
the expected transcript format depends on the context, e.g., 1945 (year) vs.
19:45 (timestamp). We compare cascaded and end-to-end approaches to recognize
and format numeric expression, such as years, timestamps, currency amounts, and
quantities. For the end-to-end approach we employed a data generation strategy
using a large language model (LLM) together with a text to speech (TTS) model
to generate adaptation data. The results on our test dataset show that while
approaches based on LLMs perform well on recognizing formatted numeric
expressions, adapted end-to-end models offer competitive performance with the
advantage of lower latency and inference cost.
