# Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin

arXiv: https://arxiv.org/abs/1806.06342 · HTML: https://mlsanity.com/paper/1806.06342/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization](https://mlsanity.com/paper/2104.02882/) (2021)
- [Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing](https://mlsanity.com/paper/2211.01522/) (2022)
- [A study of latent monotonic attention variants](https://mlsanity.com/paper/2103.16710/) (2021)
- [Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition](https://mlsanity.com/paper/2304.04991/) (2023)
- [GNCformer Enhanced Self-attention for Automatic Speech Recognition](https://mlsanity.com/paper/2305.12755/) (2023)
- [Hijacking Attacks against Neural Networks by Analyzing Training Data](https://mlsanity.com/paper/2401.09740/) (2024)

## Abstract (arXiv)

End-to-end models have been showing superiority in Automatic Speech Recognition (ASR). At the same time, the capacity of streaming recognition has become a growing requirement for end-to-end models. Following these trends, an encoder-decoder recurrent neural network called Recurrent Neural Aligner (RNA) has been freshly proposed and shown its competitiveness on two English ASR tasks. However, it is not clear if RNA can be further improved and applied to other spoken language. In this work, we explore the applicability of RNA in Mandarin Chinese and present four effective extensions: In the encoder, we redesign the temporal down-sampling and introduce a powerful convolutional structure. In the decoder, we utilize a regularizer to smooth the output distribution and conduct joint training with a language model. On two Mandarin Chinese conversational telephone speech recognition (MTS) datasets, our Extended-RNA obtains promising performance. Particularly, it achieves 27.7% character error rate (CER), which is superior to current state-of-the-art result on the popular HKUST task.