# VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording

arXiv: https://arxiv.org/abs/2107.07509 · HTML: https://mlsanity.com/paper/2107.07509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 45 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 27 of 27)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [Correcting Length Bias in Neural Machine Translation](https://mlsanity.com/paper/1808.10006/) (2018)
- [Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset](https://mlsanity.com/paper/2010.11395/) (2020)
- [Online and Linear-Time Attention by Enforcing Monotonic Alignments](https://mlsanity.com/paper/1704.00784/) (2017)
- [Monotonic Chunkwise Attention](https://mlsanity.com/paper/1712.05382/) (2017)
- [CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition](https://mlsanity.com/paper/1905.11235/) (2019)
- [Recognizing long-form speech using streaming end-to-end models](https://mlsanity.com/paper/1910.11455/) (2019)
- [FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization](https://mlsanity.com/paper/2010.11148/) (2020)
- [Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01818/) (2017)
- [On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition](https://mlsanity.com/paper/2005.14327/) (2020)
- [Towards Fast and Accurate Streaming End-to-End ASR](https://mlsanity.com/paper/2004.11544/) (2020)
- [Exploring Neural Transducers for End-to-End Speech Recognition](https://mlsanity.com/paper/1707.07413/) (2017)
- [A comparison of end-to-end models for long-form speech recognition](https://mlsanity.com/paper/1911.02242/) (2019)
- [Alignment Restricted Streaming Recurrent Neural Network Transducer](https://mlsanity.com/paper/2011.03072/) (2020)
- [Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping](https://mlsanity.com/paper/1902.06450/) (2019)
- [End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection](https://mlsanity.com/paper/2002.00551/) (2020)
- [Low Latency End-to-End Streaming Speech Recognition with a Scout Network](https://mlsanity.com/paper/2003.10369/) (2020)
- [Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR](https://mlsanity.com/paper/2004.05009/) (2020)
- [Highway Long Short-Term Memory RNNs for Distant Speech Recognition](https://mlsanity.com/paper/1510.08983/) (2015)
- [Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition](https://mlsanity.com/paper/2103.00422/) (2021)
- [A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition](https://mlsanity.com/paper/2005.10113/) (2020)
- [CTC-synchronous Training for Monotonic Attention Model](https://mlsanity.com/paper/2005.04712/) (2020)
- [StableEmit: Selection Probability Discount for Reducing Emission Latency of Streaming Monotonic Attention ASR](https://mlsanity.com/paper/2107.00635/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Speech Dialogue Translation Mediating Speakers of Different Languages](https://mlsanity.com/paper/2305.09210/) (2023)

## Abstract (arXiv)

In this work, we propose novel decoding algorithms to enable streaming automatic speech recognition (ASR) on unsegmented long-form recordings without voice activity detection (VAD), based on monotonic chunkwise attention (MoChA) with an auxiliary connectionist temporal classification (CTC) objective. We propose a block-synchronous beam search decoding to take advantage of efficient batched output-synchronous and low-latency input-synchronous searches. We also propose a VAD-free inference algorithm that leverages CTC probabilities to determine a suitable timing to reset the model states to tackle the vulnerability to long-form data. Experimental evaluations demonstrate that the block-synchronous decoding achieves comparable accuracy to the label-synchronous one. Moreover, the VAD-free inference can recognize long-form speech robustly for up to a few hours.