Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition

02/02/2021
by   Zhong Meng, et al.
0

The efficacy of external language model (LM) integration with existing end-to-end (E2E) automatic speech recognition (ASR) systems can be improved significantly using the internal language model estimation (ILME) method. In this method, the internal LM score is subtracted from the score obtained by interpolating the E2E score with the external LM score, during inference. To improve the ILME-based inference, we propose an internal LM training (ILMT) method to minimize an additional internal LM loss by updating only the E2E model components that affect the internal LM estimation. ILMT encourages the E2E model to form a standalone LM inside its existing components, without sacrificing ASR accuracy. After ILMT, the more modular E2E model with matched training and inference criteria enables a more thorough elimination of the source-domain internal LM, and therefore leads to a more effective integration of the target-domain external LM. Experimented with 30K-hour trained recurrent neural network transducer and attention-based encoder-decoder models, ILMT with ILME-based inference achieves up to 31.5 reductions from standard E2E training with Shallow Fusion on out-of-domain LibriSpeech and in-domain Microsoft production test sets, respectively.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

11/03/2020

Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition

The external language models (LM) integration remains a challenging task...
01/26/2022

Internal language model estimation through explicit context vector learning for attention-based encoder-decoder ASR

An end-to-end (E2E) speech recognition model implicitly learns a biased ...
04/07/2021

Librispeech Transducer Model with Internal Language Model Prior Correction

We present our transducer model on Librispeech. We study variants to inc...
10/06/2021

Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition

Text-only adaptation of an end-to-end (E2E) model remains a challenging ...
02/12/2022

USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder

Improving end-to-end speech recognition by incorporating external text d...
03/12/2020

Hybrid Autoregressive Transducer (hat)

This paper proposes and evaluates the hybrid autoregressive transducer (...
04/12/2021

Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models

Attention-based encoder-decoder (AED) models learn an implicit internal ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.