# Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR

arXiv: https://arxiv.org/abs/2403.10937 · HTML: https://mlsanity.com/paper/2403.10937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 80 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Speech Recognition with Augmented Synthesized Speech](https://mlsanity.com/paper/1909.11699/) (2019)
- [Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems](https://mlsanity.com/paper/1912.09257/) (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)
- [MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition](https://mlsanity.com/paper/2102.12664/) (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)
- [Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus](https://mlsanity.com/paper/2008.10491/) (2020)
- [Training Neural Speech Recognition Systems with Synthetic Speech Augmentation](https://mlsanity.com/paper/1811.00707/) (2018)
- [Training Keyword Spotters with Limited and Synthesized Speech Data](https://mlsanity.com/paper/2002.01322/) (2020)
- [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)
- [LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring](https://mlsanity.com/paper/1907.01030/) (2019)
- [Connecting and Comparing Language Model Interpolation Techniques](https://mlsanity.com/paper/1908.09738/) (2019)
- [Data Augmentation For Children's Speech Recognition -- The "Ethiopian" System For The SLT 2021 Children Speech Recognition Challenge](https://mlsanity.com/paper/2011.04547/) (2020)
- [Text Augmentation for Language Models in High Error Recognition Scenario](https://mlsanity.com/paper/2011.06056/) (2020)

## Abstract (arXiv)

This paper addresses the problem of improving speech recognition accuracy with lattice rescoring in low-resource languages where the baseline language model is insufficient for generating inclusive lattices. We minimally augment the baseline language model with word unigram counts that are present in a larger text corpus of the target language but absent in the baseline. The lattices generated after decoding with such an augmented baseline language model are more comprehensive. We obtain 21.8% (Telugu) and 41.8% (Kannada) relative word error reduction with our proposed method. This reduction in word error rate is comparable to 21.5% (Telugu) and 45.9% (Kannada) relative word error reduction obtained by decoding with full Wikipedia text augmented language mode while our approach consumes only 1/8th the memory. We demonstrate that our method is comparable with various text selection-based language model augmentation and also consistent for data sets of different sizes. Our approach is applicable for training speech recognition systems under low resource conditions where speech data and compute resources are insufficient, while there is a large text corpus that is available in the target language. Our research involves addressing the issue of out-of-vocabulary words of the baseline in general and does not focus on resolving the absence of named entities. Our proposed method is simple and yet computationally less expensive.