# Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition

arXiv: https://arxiv.org/abs/2010.16368 · HTML: https://mlsanity.com/paper/2010.16368/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 20 of 20)

- [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)
- [Librispeech Transducer Model with Internal Language Model Prior Correction](https://mlsanity.com/paper/2104.03006/) (2021)
- [On Language Model Integration for RNN Transducer based Speech Recognition](https://mlsanity.com/paper/2110.06841/) (2021)
- [Conformer-based Hybrid ASR System for Switchboard Dataset](https://mlsanity.com/paper/2111.03442/) (2021)
- [Efficient Training of Neural Transducer for Speech Recognition](https://mlsanity.com/paper/2204.10586/) (2022)
- [Decoder-only Architecture for Streaming End-to-end Speech Recognition](https://mlsanity.com/paper/2406.16107/) (2024)
- [Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition](https://mlsanity.com/paper/2104.09106/) (2021)
- [On Architectures and Training for Raw Waveform Feature Extraction in ASR](https://mlsanity.com/paper/2104.04298/) (2021)
- [Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures](https://mlsanity.com/paper/2104.05379/) (2021)
- [Dynamic Encoder Size Based on Data-Driven Layer-wise Pruning for Speech Recognition](https://mlsanity.com/paper/2407.18930/) (2024)
- [Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept](https://mlsanity.com/paper/2104.06104/) (2021)
- [Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers](https://mlsanity.com/paper/2107.03007/) (2021)
- [Monotonic segmental attention for automatic speech recognition](https://mlsanity.com/paper/2210.14742/) (2022)
- [Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers](https://mlsanity.com/paper/2212.04325/) (2022)
- [RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition](https://mlsanity.com/paper/2305.17782/) (2023)
- [On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition](https://mlsanity.com/paper/2407.21476/) (2024)
- [On the Relation between Internal Language Model and Sequence Discriminative Training for Neural Transducers](https://mlsanity.com/paper/2309.14130/) (2023)
- [Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression](https://mlsanity.com/paper/2501.18895/) (2025)
- [Investigating the Effect of Language Models in Sequence Discriminative Training for Neural Transducers](https://mlsanity.com/paper/2310.07345/) (2023)

## Abstract (arXiv)

To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeling. Different alignment label topologies are compared and word-end-based phoneme label augmentation is proposed to improve performance. Utilizing the local dependency of phonemes, we adopt a simplified neural network structure and a straightforward integration with the external word-level language model to preserve the consistency of seq-to-seq modeling. We also present a simple, stable and efficient training procedure using frame-wise cross-entropy loss. A phonetic context size of one is shown to be sufficient for the best performance. A simplified scheduled sampling approach is applied for further improvement and different decoding approaches are briefly compared. The overall performance of our best model is comparable to state-of-the-art (SOTA) results for the TED-LIUM Release 2 and Switchboard corpora.