# A Review of Deep Learning Techniques for Speech Processing

arXiv: https://arxiv.org/abs/2305.00359 · HTML: https://mlsanity.com/paper/2305.00359/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2023.

## Built on by (most cited first, 30 of 52)

- [Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures](https://mlsanity.com/paper/2307.15220/) (2023)
- [The Age of Synthetic Realities: Challenges and Opportunities](https://mlsanity.com/paper/2306.11503/) (2023)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models](https://mlsanity.com/paper/2404.00569/) (2024)
- [Audio-Visual Speech Enhancement with Score-Based Generative Models](https://mlsanity.com/paper/2306.01432/) (2023)
- [ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation](https://mlsanity.com/paper/2305.18028/) (2023)
- [Model Compression Method for S4 with Diagonal State Space Layers using Balanced Truncation](https://mlsanity.com/paper/2402.15993/) (2024)
- [MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation](https://mlsanity.com/paper/2505.17613/) (2025)
- [Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation](https://mlsanity.com/paper/2506.12879/) (2025)
- [A Survey of Deep Learning Audio Generation Methods](https://mlsanity.com/paper/2406.00146/) (2024)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [RoDia: A New Dataset for Romanian Dialect Identification from Speech](https://mlsanity.com/paper/2309.03378/) (2023)
- [Provable In-Context Learning of Nonlinear Regression with Transformers](https://mlsanity.com/paper/2507.20443/) (2025)
- [The Art of Deception: Robust Backdoor Attack using Dynamic Stacking of Triggers](https://mlsanity.com/paper/2401.01537/) (2024)
- [Sound Check: Auditing Audio Datasets](https://mlsanity.com/paper/2410.13114/) (2024)
- [DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech](https://mlsanity.com/paper/2410.13342/) (2024)
- [How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures](https://mlsanity.com/paper/2507.05885/) (2025)
- [Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer](https://mlsanity.com/paper/2405.09470/) (2024)
- [A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations](https://mlsanity.com/paper/2408.09202/) (2024)
- [Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic Environments](https://mlsanity.com/paper/2409.10048/) (2024)
- [A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference](https://mlsanity.com/paper/2502.10089/) (2025)
- [NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids](https://mlsanity.com/paper/2502.10822/) (2025)
- [Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems](https://mlsanity.com/paper/2504.00858/) (2025)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (2025)
- [BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing](https://mlsanity.com/paper/2506.03515/) (2025)
- [Multi-interaction TTS toward professional recording reproduction](https://mlsanity.com/paper/2507.00808/) (2025)
- [Learning Human Motion with Temporally Conditional Mamba](https://mlsanity.com/paper/2510.12573/) (2025)
- [Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding](https://mlsanity.com/paper/2305.12301/) (2023)
- [Pathway to a fully data-driven geotechnics: lessons from materials informatics](https://mlsanity.com/paper/2312.00581/) (2023)
- [HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworks](https://mlsanity.com/paper/2404.04645/) (2024)

## Abstract (arXiv)

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field.