# Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses

arXiv: https://arxiv.org/abs/2001.05284 · HTML: https://mlsanity.com/paper/2001.05284/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models](https://mlsanity.com/paper/2309.15701/) (2023)
- [Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems](https://mlsanity.com/paper/2112.05842/) (2021)
- [PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling](https://mlsanity.com/paper/2208.11508/) (2022)
- [An Approach to Improve Robustness of NLP Systems against ASR Errors](https://mlsanity.com/paper/2103.13610/) (2021)
- [OLISIA: a Cascade System for Spoken Dialogue State Tracking](https://mlsanity.com/paper/2304.11073/) (2023)
- [Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations](https://mlsanity.com/paper/2310.03518/) (2023)
- [DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task](https://mlsanity.com/paper/2310.10169/) (2023)
- [A Token-Wise Beam Search Algorithm for RNN-T](https://mlsanity.com/paper/2302.14357/) (2023)
- [End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English](https://mlsanity.com/paper/2211.07710/) (2022)
- [LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech Recognition](https://mlsanity.com/paper/2406.04432/) (2024)
- [Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis](https://mlsanity.com/paper/2203.12067/) (2022)
- [Recent Progress in Conversational AI](https://mlsanity.com/paper/2204.09719/) (2022)

## Abstract (arXiv)

In a modern spoken language understanding (SLU) system, the natural language understanding (NLU) module takes interpretations of a speech from the automatic speech recognition (ASR) module as the input. The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification. However, the ASR module might misrecognize some speeches and the first best interpretation could be erroneous and noisy. Solely relying on the first best interpretation could make the performance of downstream tasks non-optimal. To address this issue, we introduce a series of simple yet efficient models for improving the understanding of semantics of the input speeches by collectively exploiting the n-best speech interpretations from the ASR module.