# Wav2Letter: an End-to-End ConvNet-based Speech Recognition System

arXiv: https://arxiv.org/abs/1609.03193 · HTML: https://mlsanity.com/paper/1609.03193/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2016.

## Built on by (most cited first, 30 of 43)

- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://mlsanity.com/paper/2109.06870/) (2021)
- [YODAS: Youtube-Oriented Dataset for Audio and Speech](https://mlsanity.com/paper/2406.00899/) (2024)
- [Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognition](https://mlsanity.com/paper/2109.01163/) (2021)
- [Why does CTC result in peaky behavior?](https://mlsanity.com/paper/2105.14849/) (2021)
- [Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages](https://mlsanity.com/paper/2205.01086/) (2022)
- [ASR2K: Speech Recognition for Around 2000 Languages without Audio](https://mlsanity.com/paper/2209.02842/) (2022)
- [Flashlight: Enabling Innovation in Tools for Machine Learning](https://mlsanity.com/paper/2201.12465/) (2022)
- [Audio-Visual Efficient Conformer for Robust Speech Recognition](https://mlsanity.com/paper/2301.01456/) (2023)
- [Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition](https://mlsanity.com/paper/2106.07759/) (2021)
- [An Improved Model for Voicing Silent Speech](https://mlsanity.com/paper/2106.01933/) (2021)
- [Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech](https://mlsanity.com/paper/2103.01032/) (2021)
- [High Fidelity Speech Regeneration with Application to Speech Enhancement](https://mlsanity.com/paper/2102.00429/) (2021)
- [Star Temporal Classification: Sequence Classification with Partially Labeled Data](https://mlsanity.com/paper/2201.12208/) (2022)
- [Global Normalization for Streaming Speech Recognition in a Modular Framework](https://mlsanity.com/paper/2205.13674/) (2022)
- [ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems](https://mlsanity.com/paper/2102.08535/) (2021)
- [Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model](https://mlsanity.com/paper/2303.00091/) (2023)
- [A Toolbox for Construction and Analysis of Speech Datasets](https://mlsanity.com/paper/2104.04896/) (2021)
- [Unsupervised Automatic Speech Recognition: A Review](https://mlsanity.com/paper/2106.04897/) (2021)
- [SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2103.10651/) (2021)
- [Sequence Transduction with Graph-based Supervision](https://mlsanity.com/paper/2111.01272/) (2021)
- [And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?](https://mlsanity.com/paper/2210.01256/) (2022)
- [BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge](https://mlsanity.com/paper/2101.12729/) (2021)
- [Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models](https://mlsanity.com/paper/2110.05968/) (2021)
- [Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework](https://mlsanity.com/paper/2307.01715/) (2023)

## Abstract (arXiv)

This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters, with transcribed speech, without the need for force alignment of phonemes. We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC while being simpler. We show competitive results in word error rate on the Librispeech corpus with MFCC features, and promising results from raw waveform.