# Multi-blank Transducers for Speech Recognition

arXiv: https://arxiv.org/abs/2211.03541 · HTML: https://mlsanity.com/paper/2211.03541/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss](https://mlsanity.com/paper/2002.02562/) (2020)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization](https://mlsanity.com/paper/2010.11148/) (2020)
- [Improving RNN Transducer Modeling for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.12415/) (2019)
- [Pruned RNN-T for fast, memory-efficient ASR training](https://mlsanity.com/paper/2206.13236/) (2022)
- [Alignment Restricted Streaming Recurrent Neural Network Transducer](https://mlsanity.com/paper/2011.03072/) (2020)
- [Espresso: A Fast End-to-end Neural Speech Recognition Toolkit](https://mlsanity.com/paper/1909.08723/) (2019)
- [Fast and parallel decoding for transducer](https://mlsanity.com/paper/2211.00484/) (2022)
- [Star Temporal Classification: Sequence Classification with Partially Labeled Data](https://mlsanity.com/paper/2201.12208/) (2022)
- [Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition](https://mlsanity.com/paper/2005.00572/) (2020)
- [ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition](https://mlsanity.com/paper/2209.14868/) (2022)

## Built on by (most cited first, 11 of 11)

- [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://mlsanity.com/paper/2304.06795/) (2023)
- [Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU](https://mlsanity.com/paper/2406.03791/) (2024)
- [Label-Looping: Highly Efficient Decoding for Transducers](https://mlsanity.com/paper/2406.06220/) (2024)
- [Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments](https://mlsanity.com/paper/2307.03354/) (2023)
- [Key Frame Mechanism For Efficient Conformer Based End-to-end Speech Recognition](https://mlsanity.com/paper/2310.14954/) (2023)
- [HAINAN: Fast and Accurate Transducer for Hybrid-Autoregressive ASR](https://mlsanity.com/paper/2410.02597/) (2024)
- [Bayes Risk Transducer: Transducer with Controllable Alignment Prediction](https://mlsanity.com/paper/2308.10107/) (2023)
- [Lightweight Transducer Based on Frame-Level Criterion](https://mlsanity.com/paper/2409.13698/) (2024)
- [WIND: Accelerated RNN-T Decoding with Windowed Inference for Non-blank Detection](https://mlsanity.com/paper/2505.13765/) (2025)
- [A Study on Regularization-Based Continual Learning Methods for Indic ASR](https://mlsanity.com/paper/2508.06280/) (2025)
- [Chunk-wise Attention Transducers for Fast and Accurate Streaming Speech-to-Text](https://mlsanity.com/paper/2602.24245/) (2026)

## Abstract (arXiv)

This paper proposes a modification to RNN-Transducer (RNN-T) models for automatic speech recognition (ASR). In standard RNN-T, the emission of a blank symbol consumes exactly one input frame; in our proposed method, we introduce additional blank symbols, which consume two or more input frames when emitted. We refer to the added symbols as big blanks, and the method multi-blank RNN-T. For training multi-blank RNN-Ts, we propose a novel logit under-normalization method in order to prioritize emissions of big blanks. With experiments on multiple languages and datasets, we show that multi-blank RNN-T methods could bring relative speedups of over +90%/+139% to model inference for English Librispeech and German Multilingual Librispeech datasets, respectively. The multi-blank RNN-T method also improves ASR accuracy consistently. We will release our implementation of the method in the NeMo (https://github.com/NVIDIA/NeMo) toolkit.