# End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection

arXiv: https://arxiv.org/abs/2002.00551 · HTML: https://mlsanity.com/paper/2002.00551/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 20 of 20)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Visual Speech Recognition for Multiple Languages in the Wild](https://mlsanity.com/paper/2202.13084/) (2022)
- [E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR](https://mlsanity.com/paper/2204.10749/) (2022)
- [How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?](https://mlsanity.com/paper/2412.18495/) (2024)
- [Updated Corpora and Benchmarks for Long-Form Speech Recognition](https://mlsanity.com/paper/2309.15013/) (2023)
- [Cross-Modal Mutual Learning for Cued Speech Recognition](https://mlsanity.com/paper/2212.01083/) (2022)
- [InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model](https://mlsanity.com/paper/2503.02969/) (2025)
- [Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation](https://mlsanity.com/paper/2203.15479/) (2022)
- [Advancing Test-Time Adaptation in Wild Acoustic Test Settings](https://mlsanity.com/paper/2310.09505/) (2023)
- [Text-To-Speech Data Augmentation for Low Resource Speech Recognition](https://mlsanity.com/paper/2204.00291/) (2022)
- [Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems](https://mlsanity.com/paper/2211.00786/) (2022)
- [Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead](https://mlsanity.com/paper/2210.14446/) (2022)
- [E2E Segmentation in a Two-Pass Cascaded Encoder ASR Model](https://mlsanity.com/paper/2211.15432/) (2022)
- [Long-form Simultaneous Speech Translation: Thesis Proposal](https://mlsanity.com/paper/2310.11141/) (2023)
- [VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording](https://mlsanity.com/paper/2107.07509/) (2021)
- [Cross-domain Voice Activity Detection with Self-Supervised Representations](https://mlsanity.com/paper/2209.11061/) (2022)
- [Lightweight Audio Segmentation for Long-form Speech Translation](https://mlsanity.com/paper/2406.10549/) (2024)
- [HydraFormer: One Encoder For All Subsampling Rates](https://mlsanity.com/paper/2408.04325/) (2024)
- [Motion-Based Handwriting Recognition and Word Reconstruction](https://mlsanity.com/paper/2101.06025/) (2021)
- [Building Accurate Low Latency ASR for Streaming Voice Search](https://mlsanity.com/paper/2305.18596/) (2023)

## Abstract (arXiv)

This paper integrates a voice activity detection (VAD) function with end-to-end automatic speech recognition toward an online speech interface and transcribing very long audio recordings. We focus on connectionist temporal classification (CTC) and its extension of CTC/attention architectures. As opposed to an attention-based architecture, input-synchronous label prediction can be performed based on a greedy search with the CTC (pre-)softmax output. This prediction includes consecutive long blank labels, which can be regarded as a non-speech region. We use the labels as a cue for detecting speech segments with simple thresholding. The threshold value is directly related to the length of a non-speech region, which is more intuitive and easier to control than conventional VAD hyperparameters. Experimental results on unsegmented data show that the proposed method outperformed the baseline methods using the conventional energy-based and neural-network-based VAD methods and achieved an RTF less than 0.2. The proposed method is publicly available.