Open research questions in Speech Recognition and Synthesis

What the literature leaves open

• Current diagnostic methods are invasive, resource-intensive, and unsuitable for continuous monitoring. The need for a non-invasive, automated, and accurate method for swallowing event localization. The lack of effective methods for capturing subtle neck movements and modeling non-linear temporal relationships.

  End-to-End Swallowing Event Localization via Blue-Channel-to-Depth Substitution in RGB-D: GRNConvNeXt-Modified AdaTAD with KAN-Chebyshev Decoder · 2025 · DOI

• Activation steering for ASR systems is an underexplored technique in improving both ASR performance and demographic fairness.

  How to get the best and fairest performance out of your ASR system using activation steering · 2026

• Together with the release of our telecommunications-domain data, these findings provide a reproducible benchmark for measuring progress in Amharic ASR while highlighting open challenges in developing robust speech technologies for real-world deployment.

  TeleASR-Amharic: A Benchmark for Robust Amharic ASR in Realistic Telecommunication Conditions · 2026

• However, the shortcut branches in residual architectures are still treated as simple identity mappings, leaving their representational potential underexplored.

  SIM-IDE-FFM: Similarity-Based Residual-Identity Feature Fusion for Speaker Verification · 2026 · DOI

• Nevertheless, it remains unclear on the mechanism of effectively predicting magnitude and phase targets jointly.

  Rethinking the Joint Estimation of Magnitude and Phase for Time-Frequency Domain Neural Vocoders · 2026 · DOI

• A multi-branch architecture is employed throughout, enabling both network-level ( macro) and block-level (micro) multi-scale modeling to better exploit limited information.

  SRC-TDNN: Split Residual Collaborative Time Delay Neural Network for Short-Speech Speaker Verification · 2026 · DOI

• However, under short-utterance conditions, two limitations arise: limited information hinders the extraction of discriminative speaker features, and the inherent one-dimensional (1D) convolutions lack translation equivariance to frequency shifts, restricting robustness and feature utilization.

  SRC-TDNN: Split Residual Collaborative Time Delay Neural Network for Short-Speech Speaker Verification · 2026 · DOI

• Automatic speech recognition (ASR) in air traffic control (ATC) is a low-resource task with limited data and difficult annotation.

  Low-Resource Speech Recognition of Radiotelephony Communications Based on Continuous Learning of In-Domain and Out-of-Domain Knowledge · 2025 · DOI

• Traditional voice cloning and text-to-speech systems have limitations, including high latency and limited real-time interaction capabilities. There is a need for a system that provides real-time streaming, efficient performance, and flexible voice cloning capabilities on standard hardware.

  Real-Time Voice Cloning and Streaming System · 2026 · DOI

• Future research will explore multi-level adversarial alignment and the development of ASR-aware TTS systems to further narrow the gap between synthetic and natural speech domains.

  JODAL: Joint Domain Adversarial Learning for TTS-Augmented Automatic Speech Recognition · 2026 · DOI

• This study introduced a HNQSM for speaker recognition, designed to address challenges in feature extraction, noise resilience, and processing efficiency. By integrating CNN, QCNN, and SNN architectures, this approach enhances both SV and SI accuracy, even in noisy environments.

  HNQSM: A Novel Hybrid Neural-Quantum Approach for Advanced Speaker Verification and Identification · 2026 · DOI

• Current detection systems lack interpretability and fail to capture speaker-specific idiosyncratic traits. There is a need for a personalized and interpretable solution for speech deepfake detection.

  Profiling the Voice: Speaker-Specific Phoneme Fingerprinting for Speech Deepfake Detection · 2026

• Converting read speech to conversational speech is a significant challenge due to the lack of prosodic features. Traditional read speech often lacks the nuanced prosodic variation essential for natural conversational interactions.

  Bridging the Gap: Converting Read Text to Conversational Dialogue · 2026

• Future work could explore stronger yet lightweight modality-aware models specifically optimized for multimodal importance estimation and dynamic strategy prediction. Future research should investigate how to remove even more redundant multimodal tokens while preserving most of the reasoning capability and performance of OmniLLMs.

  OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models · 2026

• The research gap is the lack of methods that address the challenges of streaming ASR and contextual biasing. Prior methods focus on offline settings and do not provide a solution for real-time contextual biasing in streaming ASR.

  Contextual Biasing for Streaming ASR via CTC-based Word Spotting · 2026

• The paper identifies a gap in existing Neural Audio Codecs (NACs) which have limited compression ratios. The paper identifies a need for a novel architecture that can achieve high compression ratios while maintaining reconstruction quality and downstream generative performance.

  SAME: A Semantically-Aligned Music Autoencoder · 2026

• The current static ordering, while effective, does not account for the temporal evolution of dataset relationships—as the model learns, two datasets that were initially conflicting might become synergistic.

  Heterogeneity-Aware Dataset Scheduling for Efficient Audio Large Language Model Training · 2026

• Finally, two potentially beneficial training variants are not explored: training PuLSS directly with estimated (rather than oracle) speaker-activity timestamps to better match inferencetime conditions, and end-to-end joint fine-tuning of PuLSS 15 together with the downstream ASR model.

  Cross-Talk Speech Reduction, by Separation, for Separation · 2026

• Lastly, the pragmatic necessity and conceptually underexplored area of speech-LLM integration for low-resource and endangered languages are both evident. Cross-modal parameter-efficient adaptation techniques that surpass the rank limits of unimodal fine-tuning are scarce.

  Integrating Speech into Large Language Models: Architectures, Training Strategies, and Emerging Challenges · 2026 · DOI

• Cross-modal attention mechanisms are demonstrated for speech-multimodal fusion in PD detection, but no study applies context-guided cross-modal attention to integrate EEG and gait modalities, where attention weights could dynamically balance neurophysiological and biomechanical signals.

  Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention · 2026

• Despite the promising results, the proposed framework has certain limitations. The evaluation is conducted on a limited set of speech samples, and the selection strategy relies on a predefined set of enhancement algorithms. In future work, noise classification performance will be further enhanced by exploring alternative acoustic features and advanced deep learning models.

  Optimizing speech quality and intelligibility through noise-aware enhancement for coding application · 2026 · DOI

• But few studies have considered whether speech characteristics are captured \textit{within} individual dimensions of SSL features.

  Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features · 2026 · DOI

• WER and CER assume that errors are local mismatches between predicted and expected tokens, - The model operates outside its training distribution, - The nature of failure changes, - The evaluation metrics used…

  When AI fails to listen: convergent failure patterns across speech impairments and low-resource speech in ASR · 2026 · DOI

• Building an evaluation chain where each tool reveals what the previous one could not, - Developing new metrics that can distinguish between different types of failures, - Investigating the causes of language boundary…

  When AI fails to listen: convergent failure patterns across speech impairments and low-resource speech in ASR · 2026 · DOI

• Evaluating the framework on a larger dataset, - Comparing the results with other state-of-the-art methods, - Assessing the clinical significance of the results, - Exploring the use of other adapter designs and decoder…

  End-to-End Swallowing Event Localization via Blue-Channel-to-Depth Substitution in RGB-D: GRNConvNeXt-Modified AdaTAD with KAN-Chebyshev Decoder · 2025 · DOI

Most-cited papers in Speech Recognition and Synthesis

• HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units · IEEE/ACM Transactions on Audio Speech and Language Processing · 2021 · 2,771 citations
• Shortlist: a connectionist model of continuous speech recognition · Cognition · 1994 · 670 citations
• Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding · 2023 · 621 citations
• Racial disparities in automated speech recognition · Proceedings of the National Academy of Sciences · 2020 · 591 citations
• Hidden semi-Markov models · Artificial Intelligence · 2009 · 545 citations
• Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU · Journal of Artificial Intelligence and Soft Computing Research · 2019 · 517 citations
• ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech · Computer Speech & Language · 2020 · 471 citations
• Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation · 2023 · 454 citations
• Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel. · Psychological Review · 2015 · 446 citations
• CLAP Learning Audio Concepts from Natural Language Supervision · 2023 · 426 citations

Most recent work

• HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models · IEEE Transactions on Mobile Computing · 2026
• Latency-Aware Pruning and Quantization of Self-Supervised Speech Transformers for Edge Devices · ACM Transactions on Embedded Computing Systems · 2026
• Deep neural network and hidden markov model hybrid automatic speech recognition system for recognizing spontaneous Kannada proverb’s with perspectives on the future of ASR research · Multimedia Tools and Applications · 2026
• SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing · IEEE Journal of Selected Topics in Signal Processing · 2026
• Arabic speech command recognition using an enhanced CNN-LSTM model with attention and data augmentation · International Journal of Speech Technology · 2026
• Optimizing automatic speech recognition with advanced transformers and metaheuristics · Biomedical Signal Processing and Control · 2026
• Text to band gap: Pre-trained language models as encoders for semiconductor band gap prediction · Computational Materials Science · 2026
• Convolutional kolmogorov-arnold network(CKAN) based spoofing countermeasure System(SCMS) · International Journal of Speech Technology · 2026
• Interpretable EDR estimation from aircraft QAR data using deep learning and transform domain features · Advanced Engineering Informatics · 2026
• Phonetic-enhanced acoustic features: a step forward in synthetic speech detection · Multimedia Tools and Applications · 2026

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