# From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition

arXiv: https://arxiv.org/abs/1910.01493 · HTML: https://mlsanity.com/paper/1910.01493/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

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

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding](https://mlsanity.com/paper/2104.02138/) (2021)
- [Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition](https://mlsanity.com/paper/2106.07759/) (2021)
- [CTC Variations Through New WFST Topologies](https://mlsanity.com/paper/2110.03098/) (2021)
- [Scaling ASR Improves Zero and Few Shot Learning](https://mlsanity.com/paper/2111.05948/) (2021)
- [Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training](https://mlsanity.com/paper/2206.10125/) (2022)
- [Efficient Streaming LLM for Speech Recognition](https://mlsanity.com/paper/2410.03752/) (2024)
- [Contrastive Semi-supervised Learning for ASR](https://mlsanity.com/paper/2103.05149/) (2021)
- [Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition](https://mlsanity.com/paper/2104.09106/) (2021)
- [Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency](https://mlsanity.com/paper/2104.02176/) (2021)
- [Collaborative Training of Acoustic Encoders for Speech Recognition](https://mlsanity.com/paper/2106.08960/) (2021)
- [On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models](https://mlsanity.com/paper/2107.04154/) (2021)
- [A Practitioner's Guide to Building ASR Models for Low-Resource Languages: A Case Study on Scottish Gaelic](https://mlsanity.com/paper/2506.04915/) (2025)
- [Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers](https://mlsanity.com/paper/2107.03007/) (2021)
- [Accented Speech Recognition Inspired by Human Perception](https://mlsanity.com/paper/2104.04627/) (2021)
- [Efficiency-oriented approaches for self-supervised speech representation learning](https://mlsanity.com/paper/2312.11142/) (2023)
- [Bilingual Streaming ASR with Grapheme units and Auxiliary Monolingual Loss](https://mlsanity.com/paper/2308.06327/) (2023)
- [Building English ASR model with regional language support](https://mlsanity.com/paper/2503.07522/) (2025)

## Abstract (arXiv)

There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence. In this work, we show for the first time that, on English, hybrid ASR systems can in fact model graphemes effectively by leveraging tied context-dependent graphemes, i.e., chenones. Our chenone-based systems significantly outperform equivalent senone baselines by 4.5% to 11.1% relative on three different English datasets. Our results on Librispeech are state-of-the-art compared to other hybrid approaches and competitive with previously published end-to-end numbers. Further analysis shows that chenones can better utilize powerful acoustic models and large training data, and require context- and position-dependent modeling to work well. Chenone-based systems also outperform senone baselines on proper noun and rare word recognition, an area where the latter is traditionally thought to have an advantage. Our work provides an alternative for end-to-end ASR and establishes that hybrid systems can be improved by dropping the reliance on phonetic knowledge.