# A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data

arXiv: https://arxiv.org/abs/2206.13240 · HTML: https://mlsanity.com/paper/2206.13240/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Streaming End-to-end Speech Recognition For Mobile Devices](https://mlsanity.com/paper/1811.06621/) (2018)
- [An analysis of incorporating an external language model into a sequence-to-sequence model](https://mlsanity.com/paper/1712.01996/) (2017)
- [Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition](https://mlsanity.com/paper/2011.01991/) (2020)
- [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](https://mlsanity.com/paper/1807.07281/) (2018)
- [Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability](https://mlsanity.com/paper/2007.15188/) (2020)
- [Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems](https://mlsanity.com/paper/1912.09257/) (2019)
- [Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities](https://mlsanity.com/paper/1912.09251/) (2019)
- [Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems](https://mlsanity.com/paper/2011.11564/) (2020)
- [SynthASR: Unlocking Synthetic Data for Speech Recognition](https://mlsanity.com/paper/2106.07803/) (2021)
- [You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation](https://mlsanity.com/paper/2005.07157/) (2020)
- [Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network](https://mlsanity.com/paper/2104.11127/) (2021)
- [Improving Performance of End-to-End ASR on Numeric Sequences](https://mlsanity.com/paper/1907.01372/) (2019)
- [Domain-aware Neural Language Models for Speech Recognition](https://mlsanity.com/paper/2101.03229/) (2021)
- [Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2005.04290/) (2020)
- [Attention based end to end Speech Recognition for Voice Search in Hindi and English](https://mlsanity.com/paper/2111.10208/) (2021)

## Built on by (most cited first, 7 of 7)

- [Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM](https://mlsanity.com/paper/2506.11089/) (2025)
- [Text Generation with Speech Synthesis for ASR Data Augmentation](https://mlsanity.com/paper/2305.16333/) (2023)
- [Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models](https://mlsanity.com/paper/2309.10707/) (2023)
- [On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode](https://mlsanity.com/paper/2206.12829/) (2022)
- [Internal Language Model Estimation based Adaptive Language Model Fusion for Domain Adaptation](https://mlsanity.com/paper/2211.00968/) (2022)
- [Investigating Cross-Domain Behaviors of BERT in Review Understanding](https://mlsanity.com/paper/2306.15123/) (2023)
- [Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR](https://mlsanity.com/paper/2604.06487/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition(ASR) has been dominated by deep learning-based end-to-end speech recognition models. These approaches require large amounts of labeled data in the form of audio-text pairs. Moreover, these models are more susceptible to domain shift as compared to traditional models. It is common practice to train generic ASR models and then adapt them to target domains using comparatively smaller data sets. We consider a more extreme case of domain adaptation where text-only corpus is available. In this work, we propose a simple baseline technique for domain adaptation in end-to-end speech recognition models. We convert the text-only corpus to audio data using single speaker Text to Speech (TTS) engine. The parallel data in the target domain is then used to fine-tune the final dense layer of generic ASR models. We show that single speaker synthetic TTS data coupled with final dense layer only fine-tuning provides reasonable improvements in word error rates. We use text data from address and e-commerce search domains to show the effectiveness of our low-cost baseline approach on CTC and attention-based models.