Decoupled Structure for Improved Adaptability of End-to-End Models

TL;DR

This paper introduces decoupled end-to-end speech recognition models that enable flexible domain adaptation by replacing the internal language model without re-training, improving performance across different domains.

Contribution

It proposes a novel decoupled structure for E2E ASR models that allows direct internal LM replacement for domain adaptation, enhancing versatility and robustness.

Findings

Achieved 15.1% relative WER reduction on TED-LIUM 2

Achieved 17.2% relative WER reduction on AESRC2020

Maintained intra-domain performance

Abstract

Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great success by jointly learning acoustic and linguistic information, it still suffers from the effect of domain shifts, thus limiting potential applications. The E2E ASR model implicitly learns an internal language model (LM) which characterises the training distribution of the source domain, and the E2E trainable nature makes the internal LM difficult to adapt to the target domain with text-only data To solve this problem, this paper proposes decoupled structures for attention-based encoder-decoder (Decoupled-AED) and neural transducer (Decoupled-Transducer) models, which can achieve flexible domain adaptation in both offline and online scenarios while maintaining robust intra-domain performance. To this end, the acoustic and linguistic parts of the E2E model decoder (or prediction network) are decoupled, making the linguistic component (i.e. internal LM) replaceable. When encountering a domain shift, the internal LM can be directly replaced during inference by a target-domain LM, without re-training or using domain-specific paired speech-text data. Experiments for E2E ASR models trained on the LibriSpeech-100h corpus showed that the proposed decoupled structure gave 15.1% and 17.2% relative word error rate reductions on the TED-LIUM 2 and AESRC2020 corpora while still maintaining performance on intra-domain data.