# Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition

arXiv: https://arxiv.org/abs/1707.06065 · HTML: https://mlsanity.com/paper/1707.06065/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 14 of 14)

- [TS-Net: OCR Trained to Switch Between Text Transcription Styles](https://mlsanity.com/paper/2103.05489/) (2021)
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- [A Unified Speaker Adaptation Approach for ASR](https://mlsanity.com/paper/2110.08545/) (2021)
- [Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations](https://mlsanity.com/paper/2111.08462/) (2021)
- [A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition](https://mlsanity.com/paper/2205.03027/) (2022)
- [Sequential Ensembling for Semantic Segmentation](https://mlsanity.com/paper/2210.05387/) (2022)
- [Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks](https://mlsanity.com/paper/2407.17834/) (2024)
- [Learning Human Motion with Temporally Conditional Mamba](https://mlsanity.com/paper/2510.12573/) (2025)
- [Closing the Gap between Single-User and Multi-User VoiceFilter-Lite](https://mlsanity.com/paper/2202.12169/) (2022)
- [IBNorm: Information-Bottleneck Inspired Normalization for Representation Learning](https://mlsanity.com/paper/2510.25262/) (2025)
- [KL Regularized Normalization Framework for Low Resource Tasks](https://mlsanity.com/paper/2212.11275/) (2022)
- [Contextual Control without Memory Growth in a Context-Switching Task](https://mlsanity.com/paper/2604.03479/) (2026)

## Abstract (arXiv)

Layer normalization is a recently introduced technique for normalizing the activities of neurons in deep neural networks to improve the training speed and stability. In this paper, we introduce a new layer normalization technique called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling in speech recognition. By dynamically generating the scaling and shifting parameters in layer normalization, DLN adapts neural acoustic models to the acoustic variability arising from various factors such as speakers, channel noises, and environments. Unlike other adaptive acoustic models, our proposed approach does not require additional adaptation data or speaker information such as i-vectors. Moreover, the model size is fixed as it dynamically generates adaptation parameters. We apply our proposed DLN to deep bidirectional LSTM acoustic models and evaluate them on two benchmark datasets for large vocabulary ASR experiments: WSJ and TED-LIUM release 2. The experimental results show that our DLN improves neural acoustic models in terms of transcription accuracy by dynamically adapting to various speakers and environments.