# Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation

arXiv: https://arxiv.org/abs/2306.08588 · HTML: https://mlsanity.com/paper/2306.08588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 47 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 30 of 33)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [Hi-Fi Multi-Speaker English TTS Dataset](https://mlsanity.com/paper/2104.01497/) (2021)
- [Contextual Adapters for Personalized Speech Recognition in Neural Transducers](https://mlsanity.com/paper/2205.13660/) (2022)
- [The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results](https://mlsanity.com/paper/2007.05916/) (2020)
- [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)
- [A3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing](https://mlsanity.com/paper/2203.09690/) (2022)
- [End-to-end Named Entity Recognition from English Speech](https://mlsanity.com/paper/2005.11184/) (2020)
- [Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition](https://mlsanity.com/paper/2012.01687/) (2020)
- [EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion](https://mlsanity.com/paper/2107.01554/) (2021)
- [VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature](https://mlsanity.com/paper/2204.00768/) (2022)
- [Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation](https://mlsanity.com/paper/1811.02356/) (2018)
- [End-to-end contextual speech recognition using class language models and a token passing decoder](https://mlsanity.com/paper/1812.02142/) (2018)
- [Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition](https://mlsanity.com/paper/2110.02220/) (2021)
- [Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification](https://mlsanity.com/paper/2209.06058/) (2022)
- [LAE: Language-Aware Encoder for Monolingual and Multilingual ASR](https://mlsanity.com/paper/2206.02093/) (2022)
- [Data Augmentation for End-to-end Code-switching Speech Recognition](https://mlsanity.com/paper/2011.02160/) (2020)
- [On Addressing Practical Challenges for RNN-Transducer](https://mlsanity.com/paper/2105.00858/) (2021)
- [Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization](https://mlsanity.com/paper/2111.15016/) (2021)
- [SpeechPainter: Text-conditioned Speech Inpainting](https://mlsanity.com/paper/2202.07273/) (2022)
- [End-to-End Code-Switching ASR for Low-Resourced Language Pairs](https://mlsanity.com/paper/1909.12681/) (2019)
- [Improving Low Resource Code-switched ASR using Augmented Code-switched TTS](https://mlsanity.com/paper/2010.05549/) (2020)
- [Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech](https://mlsanity.com/paper/1807.10945/) (2018)
- [Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2201.02550/) (2022)

## Built on by (most cited first, 2 of 2)

- [Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM](https://mlsanity.com/paper/2411.13159/) (2024)
- [Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer](https://mlsanity.com/paper/2309.07648/) (2023)

## Abstract (arXiv)

Recently, end-to-end (E2E) automatic speech recognition (ASR) models have made great strides and exhibit excellent performance in general speech recognition. However, there remain several challenging scenarios that E2E models are not competent in, such as code-switching and named entity recognition (NER). Data augmentation is a common and effective practice for these two scenarios. However, the current data augmentation methods mainly rely on audio splicing and text-to-speech (TTS) models, which might result in discontinuous, unrealistic, and less diversified speech. To mitigate these potential issues, we propose a novel data augmentation method by applying the text-based speech editing model. The augmented speech from speech editing systems is more coherent and diversified, also more akin to real speech. The experimental results on code-switching and NER tasks show that our proposed method can significantly outperform the audio splicing and neural TTS based data augmentation systems.