# Leveraging End-to-End Speech Recognition with Neural Architecture Search

arXiv: https://arxiv.org/abs/1912.05946 · HTML: https://mlsanity.com/paper/1912.05946/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing](https://mlsanity.com/paper/2310.01180/) (2023)
- [Mispronunciation Detection of Basic Quranic Recitation Rules using Deep Learning](https://mlsanity.com/paper/2305.06429/) (2023)
- [Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.04429/) (2023)
- [Breaking Robustness Barriers in Cognitive Diagnosis: A One-Shot Neural Architecture Search Perspective](https://mlsanity.com/paper/2601.04918/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) have been demonstrated to outperform many traditional machine learning algorithms in Automatic Speech Recognition (ASR). In this paper, we show that a large improvement in the accuracy of deep speech models can be achieved with effective Neural Architecture Optimization at a very low computational cost. Phone recognition tests with the popular LibriSpeech and TIMIT benchmarks proved this fact by displaying the ability to discover and train novel candidate models within a few hours (less than a day) many times faster than the attention-based seq2seq models. Our method achieves test error of 7% Word Error Rate (WER) on the LibriSpeech corpus and 13% Phone Error Rate (PER) on the TIMIT corpus, on par with state-of-the-art results.