# Recent Progresses in Deep Learning based Acoustic Models (Updated)

arXiv: https://arxiv.org/abs/1804.09298 · HTML: https://mlsanity.com/paper/1804.09298/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 8 of 8)

- [SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts](https://mlsanity.com/paper/2105.03036/) (2021)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Aligning Source Visual and Target Language Domains for Unpaired Video Captioning](https://mlsanity.com/paper/2211.12148/) (2022)
- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [Towards Improved Room Impulse Response Estimation for Speech Recognition](https://mlsanity.com/paper/2211.04473/) (2022)
- [ProGRes: Prompted Generative Rescoring on ASR n-Best](https://mlsanity.com/paper/2409.00217/) (2024)
- [STRODE: Stochastic Boundary Ordinary Differential Equation](https://mlsanity.com/paper/2107.08273/) (2021)
- [Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model](https://mlsanity.com/paper/2107.01571/) (2021)

## Abstract (arXiv)

In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. We first discuss acoustic models that can effectively exploit variable-length contextual information, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and their various combination with other models. We then describe acoustic models that are optimized end-to-end with emphasis on feature representations learned jointly with rest of the system, the connectionist temporal classification (CTC) criterion, and the attention-based sequence-to-sequence model. We further illustrate robustness issues in speech recognition systems, and discuss acoustic model adaptation, speech enhancement and separation, and robust training strategies. We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.