# Online Automatic Speech Recognition with Listen, Attend and Spell Model

arXiv: https://arxiv.org/abs/2008.05514 · HTML: https://mlsanity.com/paper/2008.05514/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition](https://mlsanity.com/paper/2103.00422/) (2021)
- [Efficient Streaming LLM for Speech Recognition](https://mlsanity.com/paper/2410.03752/) (2024)
- [ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition](https://mlsanity.com/paper/2209.14868/) (2022)
- [Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition](https://mlsanity.com/paper/2309.08436/) (2023)
- [Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition](https://mlsanity.com/paper/2102.07259/) (2021)
- [Bilingual End-to-End ASR with Byte-Level Subwords](https://mlsanity.com/paper/2205.00485/) (2022)
- [Monotonic segmental attention for automatic speech recognition](https://mlsanity.com/paper/2210.14742/) (2022)
- [Provable Robustness for Streaming Models with a Sliding Window](https://mlsanity.com/paper/2303.16308/) (2023)
- [Beyond Fresh Starts: Stateful Inference for Streaming ASR in Conversational Voice Agents](https://mlsanity.com/paper/2608.22101/) (2026)

## Abstract (arXiv)

The Listen, Attend and Spell (LAS) model and other attention-based automatic speech recognition (ASR) models have known limitations when operated in a fully online mode. In this paper, we analyze the online operation of LAS models to demonstrate that these limitations stem from the handling of silence regions and the reliability of online attention mechanism at the edge of input buffers. We propose a novel and simple technique that can achieve fully online recognition while meeting accuracy and latency targets. For the Mandarin dictation task, our proposed approach can achieve a character error rate in online operation that is within 4% relative to an offline LAS model. The proposed online LAS model operates at 12% lower latency relative to a conventional neural network hidden Markov model hybrid of comparable accuracy. We have validated the proposed method through a production scale deployment, which, to the best of our knowledge, is the first such deployment of a fully online LAS model.