# Contextual Speech Recognition with Difficult Negative Training Examples

arXiv: https://arxiv.org/abs/1810.12170 · HTML: https://mlsanity.com/paper/1810.12170/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2018.

## Built on by (most cited first, 28 of 28)

- [Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection](https://mlsanity.com/paper/2201.12806/) (2022)
- [Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2109.00627/) (2021)
- [Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition](https://mlsanity.com/paper/2110.02220/) (2021)
- [Can Contextual Biasing Remain Effective with Whisper and GPT-2?](https://mlsanity.com/paper/2306.01942/) (2023)
- [Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition](https://mlsanity.com/paper/2207.00857/) (2022)
- [Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model](https://mlsanity.com/paper/2209.01250/) (2022)
- [CopyNE: Better Contextual ASR by Copying Named Entities](https://mlsanity.com/paper/2305.12839/) (2023)
- [Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator](https://mlsanity.com/paper/2205.09058/) (2022)
- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [Spell my name: keyword boosted speech recognition](https://mlsanity.com/paper/2110.02791/) (2021)
- [Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm](https://mlsanity.com/paper/2310.00178/) (2023)
- [Approximate Nearest Neighbour Phrase Mining for Contextual Speech Recognition](https://mlsanity.com/paper/2304.08862/) (2023)
- [End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system](https://mlsanity.com/paper/2202.09003/) (2022)
- [Retrieve and Copy: Scaling ASR Personalization to Large Catalogs](https://mlsanity.com/paper/2311.08402/) (2023)
- [End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator](https://mlsanity.com/paper/2210.16554/) (2022)
- [Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator](https://mlsanity.com/paper/2305.18824/) (2023)
- [Keyword-Guided Adaptation of Automatic Speech Recognition](https://mlsanity.com/paper/2406.02649/) (2024)
- [Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?](https://mlsanity.com/paper/2210.13189/) (2022)
- [Locality enhanced dynamic biasing and sampling strategies for contextual ASR](https://mlsanity.com/paper/2401.13146/) (2024)
- [Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval](https://mlsanity.com/paper/2411.00664/) (2024)
- [The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning](https://mlsanity.com/paper/2310.00141/) (2023)
- [Deep CLAS: Deep Contextual Listen, Attend and Spell](https://mlsanity.com/paper/2409.17603/) (2024)
- [Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview](https://mlsanity.com/paper/2403.00370/) (2024)
- [Improving Contextual ASR via Multi-grained Fusion with Large Language Models](https://mlsanity.com/paper/2507.12252/) (2025)
- [Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modeling](https://mlsanity.com/paper/2509.05908/) (2025)
- [Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization](https://mlsanity.com/paper/2309.17267/) (2023)
- [JSPG: Dynamic Dictionary Filtering via Joint Semantic-Pinyin-Glyph Retrieval for Chinese Contextual ASR](https://mlsanity.com/paper/2605.16896/) (2026)
- [No Detectable Change in Side-Level WER from Prompt-Level Context: A Preregistered Ablation on a Production Oral-History Corpus](https://mlsanity.com/paper/2608.28875/) (2026)

## Abstract (arXiv)

Improving the representation of contextual information is key to unlocking the potential of end-to-end (E2E) automatic speech recognition (ASR). In this work, we present a novel and simple approach for training an ASR context mechanism with difficult negative examples. The main idea is to focus on proper nouns (e.g., unique entities such as names of people and places) in the reference transcript, and use phonetically similar phrases as negative examples, encouraging the neural model to learn more discriminative representations. We apply our approach to an end-to-end contextual ASR model that jointly learns to transcribe and select the correct context items, and show that our proposed method gives up to 53.1% relative improvement in word error rate (WER) across several benchmarks.