# No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models

arXiv: https://arxiv.org/abs/1712.01864 · HTML: https://mlsanity.com/paper/1712.01864/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2017.

## Built on by (most cited first, 13 of 13)

- [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)
- [Contextual Adapters for Personalized Speech Recognition in Neural Transducers](https://mlsanity.com/paper/2205.13660/) (2022)
- [Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition](https://mlsanity.com/paper/2305.05271/) (2023)
- [Approximate Nearest Neighbour Phrase Mining for Contextual Speech Recognition](https://mlsanity.com/paper/2304.08862/) (2023)
- [Dialog act guided contextual adapter for personalized speech recognition](https://mlsanity.com/paper/2303.17799/) (2023)
- [Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages](https://mlsanity.com/paper/2307.00759/) (2023)
- [DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition](https://mlsanity.com/paper/2403.17645/) (2024)
- [Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval](https://mlsanity.com/paper/2411.00664/) (2024)
- [Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition](https://mlsanity.com/paper/2304.01905/) (2023)
- [An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition](https://mlsanity.com/paper/2409.06468/) (2024)
- [Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting](https://mlsanity.com/paper/2210.09510/) (2022)
- [Record Deduplication for Entity Distribution Modeling in ASR Transcripts](https://mlsanity.com/paper/2306.06246/) (2023)

## Abstract (arXiv)

For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which typically predict graphemes or words, simplify the recognition process since they remove the need for a separate expert-curated pronunciation lexicon to map from phoneme-based units to words. However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units. In this work, we conduct detailed experiments which are aimed at quantifying the value of phoneme-based pronunciation lexica in the context of end-to-end models. We examine phoneme-based end-to-end models, which are contrasted against grapheme-based ones on a large vocabulary English Voice-search task, where we find that graphemes do indeed outperform phonemes. We also compare grapheme and phoneme-based approaches on a multi-dialect English task, which once again confirm the superiority of graphemes, greatly simplifying the system for recognizing multiple dialects.