  We present a modular approach to building cascade speech translation (AST)
models that guarantees that the resulting model performs no worse than the
1-best cascade baseline while preserving state-of-the-art speech recognition
(ASR) and text translation (MT) performance for a given task. Our novel
contribution is the use of an ``exporter'' layer that is trained under L2-loss
to ensure a strong match between ASR embeddings and the MT token embeddings for
the 1-best sequence. The ``exporter'' output embeddings are fed directly to the
MT model in lieu of 1-best token embeddings, thus guaranteeing that the
resulting model performs no worse than the 1-best cascade baseline, while
allowing back-propagation gradient to flow from the MT model into the ASR
components. The matched-embeddings cascade architecture provide a significant
improvement over its 1-best counterpart in scenarios where incremental training
of the MT model is not an option and yet we seek to improve quality by
leveraging (speech, transcription, translated transcription) data provided with
the AST task. The gain disappears when the MT model is incrementally trained on
the parallel text data available with the AST task. The approach holds promise
for other scenarios that seek to couple ASR encoders and immutable text models,
such at large language models (LLM).
