# Advances in All-Neural Speech Recognition

arXiv: https://arxiv.org/abs/1609.05935 · HTML: https://mlsanity.com/paper/1609.05935/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2016.

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

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units](https://mlsanity.com/paper/2110.04109/) (2021)
- [Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation](https://mlsanity.com/paper/2102.07935/) (2021)
- [End-to-End Automatic Speech Recognition with Deep Mutual Learning](https://mlsanity.com/paper/2102.08154/) (2021)
- [Exploring CTC Based End-to-End Techniques for Myanmar Speech Recognition](https://mlsanity.com/paper/2105.06253/) (2021)

## Abstract (arXiv)

This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology.