mlsanityData to 7 Oct 2026

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

arXiv 2001.00378 ↗First posted 2 Jan 2020cs.SD cs.LG eess.AScited by 28

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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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 9; 2023: 4; 2024: 0; 2025: 1; 2026: 1.

28 papers.

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  1. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 258
  2. 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 113
  3. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  4. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  5. 2022Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Datacited by 27
  6. 2021StutterNet: Stuttering Detection Using Time Delay Neural Networkcited by 20
  7. 2021Machine Learning for Stuttering Identification: Review, Challenges and Future Directionscited by 18
  8. 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
  9. 2022Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decompositioncited by 12
  10. 2022Towards Relation Extraction From Speechcited by 10
  11. 2022Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlookcited by 10
  12. 2021Configurable Privacy-Preserving Automatic Speech Recognitioncited by 7
16 more
  1. 2021It's not what you said, it's how you said it: discriminative perception of speech as a multichannel communication systemcited by 6
  2. 2023Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learningcited by 5
  3. 2021BERT Attends the Conversation: Improving Low-Resource Conversational ASRcited by 4
  4. 2021A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognitioncited by 2
  5. 2021Do Neural Network Weights account for Classes Centers?cited by 2
  6. 2025Safeguarding Privacy in Edge Speech Understanding with Tiny Foundation Modelscited by 2
  7. 2021Unsupervised Speech Representation Learning for Behavior Modeling using Triplet Enhanced Contextualized Networkscited by 1
  8. 2021Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutionscited by 1
  9. 2022Cross-domain Voice Activity Detection with Self-Supervised Representationscited by 1
  10. 2021Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutionscited by 0
  11. 2021Speech Representations and Phoneme Classification for Preserving the Endangered Language of Ladincited by 0
  12. 2022Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognitioncited by 0
  13. 2022Application of Knowledge Distillation to Multi-task Speech Representation Learningcited by 0
  14. 2022Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motorscited by 0
  15. 2023Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Datacited by 0
  16. 2026Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognitioncited by 0