# Bayes Risk Transducer: Transducer with Controllable Alignment Prediction

arXiv: https://arxiv.org/abs/2308.10107 · HTML: https://mlsanity.com/paper/2308.10107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition](https://mlsanity.com/paper/2010.10759/) (2020)
- [Hybrid Autoregressive Transducer (hat)](https://mlsanity.com/paper/2003.07705/) (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)
- [Two-Pass End-to-End Speech Recognition](https://mlsanity.com/paper/1908.10992/) (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)
- [A New Training Pipeline for an Improved Neural Transducer](https://mlsanity.com/paper/2005.09319/) (2020)
- [Reducing Streaming ASR Model Delay with Self Alignment](https://mlsanity.com/paper/2105.05005/) (2021)
- [Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks](https://mlsanity.com/paper/2210.07499/) (2022)
- [Multi-blank Transducers for Speech Recognition](https://mlsanity.com/paper/2211.03541/) (2022)
- [Minimum Latency Training of Sequence Transducers for Streaming End-to-End Speech Recognition](https://mlsanity.com/paper/2211.02333/) (2022)
- [Delay-penalized transducer for low-latency streaming ASR](https://mlsanity.com/paper/2211.00490/) (2022)
- [Sequence Transduction with Graph-based Supervision](https://mlsanity.com/paper/2111.01272/) (2021)

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

- [Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers](https://mlsanity.com/paper/2502.05232/) (2025)

## Abstract (arXiv)

Automatic speech recognition (ASR) based on transducers is widely used. In training, a transducer maximizes the summed posteriors of all paths. The path with the highest posterior is commonly defined as the predicted alignment between the speech and the transcription. While the vanilla transducer does not have a prior preference for any of the valid paths, this work intends to enforce the preferred paths and achieve controllable alignment prediction. Specifically, this work proposes Bayes Risk Transducer (BRT), which uses a Bayes risk function to set lower risk values to the preferred paths so that the predicted alignment is more likely to satisfy specific desired properties. We further demonstrate that these predicted alignments with intentionally designed properties can provide practical advantages over the vanilla transducer. Experimentally, the proposed BRT saves inference cost by up to 46% for non-streaming ASR and reduces overall system latency by 41% for streaming ASR.