  End-to-end (E2E) automatic speech recognition (ASR) systems directly map
acoustics to words using a unified model. Previous works mostly focus on E2E
training a single model which integrates acoustic and language model into a
whole. Although E2E training benefits from sequence modeling and simplified
decoding pipelines, large amount of transcribed acoustic data is usually
required, and traditional acoustic and language modelling techniques cannot be
utilized. In this paper, a novel modular training framework of E2E ASR is
proposed to separately train neural acoustic and language models during
training stage, while still performing end-to-end inference in decoding stage.
Here, an acoustics-to-phoneme model (A2P) and a phoneme-to-word model (P2W) are
trained using acoustic data and text data respectively. A phone synchronous
decoding (PSD) module is inserted between A2P and P2W to reduce sequence
lengths without precision loss. Finally, modules are integrated into an
acousticsto-word model (A2W) and jointly optimized using acoustic data to
retain the advantage of sequence modeling. Experiments on a 300- hour
Switchboard task show significant improvement over the direct A2W model. The
efficiency in both training and decoding also benefits from the proposed
method.
