# Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends

arXiv: https://arxiv.org/abs/2001.00378 · HTML: https://mlsanity.com/paper/2001.00378/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 28 of 28)

- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data](https://mlsanity.com/paper/2206.02353/) (2022)
- [StutterNet: Stuttering Detection Using Time Delay Neural Network](https://mlsanity.com/paper/2105.05599/) (2021)
- [Machine Learning for Stuttering Identification: Review, Challenges and Future Directions](https://mlsanity.com/paper/2107.04057/) (2021)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition](https://mlsanity.com/paper/2204.00465/) (2022)
- [Towards Relation Extraction From Speech](https://mlsanity.com/paper/2210.08759/) (2022)
- [Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook](https://mlsanity.com/paper/2210.13623/) (2022)
- [Configurable Privacy-Preserving Automatic Speech Recognition](https://mlsanity.com/paper/2104.00766/) (2021)
- [It's not what you said, it's how you said it: discriminative perception of speech as a multichannel communication system](https://mlsanity.com/paper/2105.00260/) (2021)
- [Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning](https://mlsanity.com/paper/2302.11343/) (2023)
- [BERT Attends the Conversation: Improving Low-Resource Conversational ASR](https://mlsanity.com/paper/2110.02267/) (2021)
- [A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2101.00738/) (2021)
- [Do Neural Network Weights account for Classes Centers?](https://mlsanity.com/paper/2104.07004/) (2021)
- [Safeguarding Privacy in Edge Speech Understanding with Tiny Foundation Models](https://mlsanity.com/paper/2502.01649/) (2025)
- [Unsupervised Speech Representation Learning for Behavior Modeling using Triplet Enhanced Contextualized Networks](https://mlsanity.com/paper/2104.03899/) (2021)
- [Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions](https://mlsanity.com/paper/2104.11985/) (2021)
- [Cross-domain Voice Activity Detection with Self-Supervised Representations](https://mlsanity.com/paper/2209.11061/) (2022)
- [Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/2106.00052/) (2021)
- [Speech Representations and Phoneme Classification for Preserving the Endangered Language of Ladin](https://mlsanity.com/paper/2108.12531/) (2021)
- [Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition](https://mlsanity.com/paper/2207.12248/) (2022)
- [Application of Knowledge Distillation to Multi-task Speech Representation Learning](https://mlsanity.com/paper/2210.16611/) (2022)
- [Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors](https://mlsanity.com/paper/2211.01716/) (2022)
- [Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Data](https://mlsanity.com/paper/2309.01574/) (2023)
- [Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognition](https://mlsanity.com/paper/2604.01711/) (2026)

## Abstract (arXiv)

Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions. There are two main drawbacks to this approach: firstly, the feature engineering being manual is cumbersome and requires human knowledge; and secondly, the designed features might not be best for the objective at hand. This has motivated the adoption of a recent trend in speech community towards utilisation of representation learning techniques, which can learn an intermediate representation of the input signal automatically that better suits the task at hand and hence lead to improved performance. The significance of representation learning has increased with advances in deep learning (DL), where the representations are more useful and less dependent on human knowledge, making it very conducive for tasks like classification, prediction, etc. The main contribution of this paper is to present an up-to-date and comprehensive survey on different techniques of speech representation learning by bringing together the scattered research across three distinct research areas including Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER). Recent reviews in speech have been conducted for ASR, SR, and SER, however, none of these has focused on the representation learning from speech -- a gap that our survey aims to bridge.