# Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems

arXiv: https://arxiv.org/abs/2005.08742 · HTML: https://mlsanity.com/paper/2005.08742/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

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

- [A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering](https://mlsanity.com/paper/2409.13483/) (2024)
- [Context-based out-of-vocabulary word recovery for ASR systems in Indian languages](https://mlsanity.com/paper/2206.04305/) (2022)

## Abstract (arXiv)

In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance. The underrepresented words correspond to rare or out-of-vocabulary (OOV) words in the training data, and thereby can't be modeled reliably. We begin with graphemic lexicon which allows to drop the necessity of phonetic models in hybrid ASR. We study it under different settings and demonstrate its effectiveness in dealing with underrepresented NEs. Next, we study the impact of neural language model (LM) with letter-based features derived to handle infrequent words. After that, we attempt to enrich representations of underrepresented NEs in pretrained neural LM by borrowing the embedding representations of rich-represented words. This let us gain significant performance improvement on underrepresented NE recognition. Finally, we boost the likelihood scores of utterances containing NEs in the word lattices rescored by neural LMs and gain further performance improvement. The combination of the aforementioned approaches improves NE recognition by up to 42% relatively.