# Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions

Source: [Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions](<https://www.sota2.com/research/paper/68fa6af91e209131cea2b75a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:37:16.577Z

Authors: Jumon Nozaki, Tatsuya Komatsu

Published: 2021-04-06T18:00:03.000Z

## Abstract

This paper proposes a method to relax the conditional independence assumption of connectionist temporal classification (CTC)-based automatic speech recognition (ASR) models. We train a CTC-based ASR model with auxiliary CTC losses in intermediate layers in addition to the original CTC loss in the last layer. During both training and inference, each generated prediction in the intermediate layers is summed to the input of the next layer to condition the prediction of the last layer on those intermediate predictions. Our method is easy to implement and retains the merits of CTC-based ASR: a simple model architecture and fast decoding speed. We conduct experiments on three different ASR corpora. Our proposed method improves a standard CTC model significantly (e.g., more than 20 % relative word error rate reduction on the WSJ corpus) with a little computational overhead. Moreover, for the TEDLIUM2 corpus and the AISHELL-1 corpus, it achieves a comparable performance to a strong autoregressive model with beam search, but the decoding speed is at least 30 times faster.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Automatic Speech Recognition on HKUST](<https://www.sota2.com/research/sota/automatic-speech-recognition-on-hkust.md>): Automatic Speech Recognition; HKUST; Sub Error Rate: 14.9.

- [Automatic Speech Recognition on LibriSpeech (test-clean)](<https://www.sota2.com/research/sota/automatic-speech-recognition-on-librispeech-test-clean.md>): Automatic Speech Recognition; Librispeech (test-clean); WER: 2.62.

- [Automatic Speech Recognition on AISHELL-1 (dev)](<https://www.sota2.com/research/sota/automatic-speech-recognition-on-aishell-1-dev.md>): Automatic Speech Recognition; AISHELL-1 (dev); CER: 4.6.

- [Automatic Speech Recognition on AISHELL-1 (test)](<https://www.sota2.com/research/sota/automatic-speech-recognition-on-aishell-1-test.md>): Automatic Speech Recognition; AISHELL-1 (test); CER: 4.9.

- [Automatic Speech Recognition on LibriSpeech (test-other)](<https://www.sota2.com/research/sota/automatic-speech-recognition-on-librispeech-test-other.md>): Automatic Speech Recognition; LibriSpeech (test-other); WER: 6.16.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.02724>)

[PDF](<https://arxiv.org/pdf/2104.02724.pdf>)