Open research questions in Speech Recognition and Synthesis

What the literature leaves open

• However, because SFMs expose representations from many layers, it remains unclear which depths are most informative for MOS prediction and how multi-layer information should be combined reliably across backbones and datasets.

  CAL-MOS: Bridging Layers with Adapters for Robust MOS Prediction Across Speech Foundation Models · 2026

• The development of automated speaking assessment (ASA) is limited by the scarcity of public datasets, with most existing work relying on read-aloud speech, which limits applicability to real-world communication scenarios.

  OpenEnded: An Open-Response Speech Corpus for Speaking Proficiency Assessment with Human Annotations and ALM Supervision · 2026

• 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

• The synthetic data, even after domain adaptation, is not a perfect substitute for real speech, - The proposed JODAL approach does not completely eliminate the domain discrepancy between real and TTS-generated speech, - Further improvements may require more advanced TTS models

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

• Extending domain adaptation beyond feature-level alignment to include model-level or task-level strategies, - Developing more advanced TTS models capable of capturing subtle speaker characteristics, coarticulation effects, and contextual prosody variations

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

• Conventional pronunciation assessment methods have limitations in handling acoustic variability. The absence of reliable self-evaluation mechanisms often results in persistent segmental and prosodic errors.

  Comparative Analysis of Japanese Speech: Applying Dynamic Time Warping and Precise Word Segmentation for Pronunciation Assessment · 2026 · DOI

• Traditional Deep Learning methods struggle to isolate unique features in noisy environments. Prior studies have limitations, such as performance degradation under non-ideal input quality. The HNQSM is introduced to address the challenges of speaker recognition in noisy environments.

  HNQSM: A Novel Hybrid Neural-Quantum Approach for Advanced Speaker Verification and Identification · 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. In SI, the hybrid model outperforms traditional CNN and LSTM models, demonstrating improvements in precision, recall, F1-score, and EER, which highlights its effectiveness in man- aging diverse and large-scale datasets. For SV, the HNQSM was benchmarked against i-vector and GMM-UBM mod- els, achieving superior EER values, which underscores its robustness in accurately verifying speakers. The comparative evaluation, including detailed metrics and runtime analyses, shows that the hybrid model provides enhanced robustness and reliability, making it well-suited for real-world appli- cations requiring precise speaker recognition. Overall, this study emphasizes the potential of hybrid DL architectures to advance speaker recognition technology, with promising 1. Telecommunications and Customer Support: HNQSM can streamline customer service processes through SI, allowing telecom providers and call centres to authen- ticate callers quickly and securely. This enhances effi- ciency in issue resolution, as customers are verified by voice alone, reducing the need for lengthy security ques- tions and improving overall service quality. 2. Smart Home and IoT Security: SI and SV using HNQSM can make smart home systems more secure, ensuring that only recognized users’ voices can unlock doors, disable alarms, or control sensitive devices, adding a layer of protection against unauthorized access to users. 3. E-Commerce and Retail: Retailers can use SI in voice- powered apps or kiosks to recognize returning customers, providing tailored recommendations and enhancing the shopping experience.

  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

• Existing Video&Text-to-Audio models struggle with counterfactual video Foley generation. The models often remain anchored to the visually implied sound source when video and text contents disagree.

  CounterFlow: A Two-Phase Inference-Time Sampling for Counterfactual Video Foley Generation · 2026

• Prior latent diffusion models operate on fixed-length sequences, requiring inference at full length. There is a need for models that support variable-length generation and editing.

  Stable Audio 3 · 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

• Existing methods rely on fixed, modality-specific guidance, which fails to account for varying importance of modalities. There is a need for a dynamic modality-aware token compression framework for Omni-LLMs.

  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

• High-dimensional and low-energy signals are difficult to model. The lack of a mechanism to map global semantic cues directly to raw waveforms limits the use of raw waveform modeling.

  WavFlow: Audio Generation in Waveform Space · 2026

• Extending WavFlow to explicit speech or singing synthesis requires finer linguistic granularity and larger speech datasets. Incorporating larger-scale corpora and fine-grained linguistic captions can improve the framework.

  WavFlow: Audio Generation in Waveform Space · 2026

• Ambient noise and room reverberation. Interference from other speakers. Limited generalizability of speech separation models trained on simulated data to real-recorded mixtures.

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

• To improve the performance of CTRnet in noisy environments. To develop a method for explicitly modeling ambient noises in the proposed framework. To evaluate the performance of the proposed framework on other datasets.

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

• Existing models often rely on simple concatenation or weighting for feature fusion. The lack of effective multi-scale modeling capability limits the discriminative power of learned embeddings.

  MSGF-ERes2Net:An Enhanced Multi-Scale Gated Feature Fusion Network for Speaker Verification · 2026 · DOI

• The paper suggests that additional research is needed to investigate real-time full-duplex communication, systematic scaling techniques for speech foundation models, and low-resource language documentation. The analysis notes that the development of novel architectural approaches and training methodologies is an area for future research.

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

• The paper identifies a gap in the existing literature, including the lack of a thorough examination of methods for incorporating speech into large language models. The analysis notes that technical obstacles remain, including the representational disparity between speech signals and LLMs.

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

• The existing video-to-audio generation methods are resource-intensive and often require large amounts of training data. There is a need for more efficient and effective methods that can achieve superior performance with reduced training data and epochs.

  Efficient Video-to-Audio Generation via Multiple Foundation Models Mapper · 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

• , identifying which TTS system produced a given audio sample) remains underexplored, particularly in open-set scenarios where unknown systems may be encountered.

  Anchoring the Unknown: Open-Set Model Attribution via Proxy-Anchor Learning · 2026

Most-cited papers in Speech Recognition and Synthesis

• 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
• 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
• 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
• AudioLM: A Language Modeling Approach to Audio Generation · IEEE/ACM Transactions on Audio Speech and Language Processing · 2023 · 377 citations
• ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild · IEEE/ACM Transactions on Audio Speech and Language Processing · 2023 · 294 citations
• Speech Enhancement and Dereverberation With Diffusion-Based Generative Models · IEEE/ACM Transactions on Audio Speech and Language Processing · 2023 · 265 citations

Most recent work

• Latency-Aware Pruning and Quantization of Self-Supervised Speech Transformers for Edge Devices · ACM Transactions on Embedded Computing Systems · 2026
• A Clinical Speech Corpus with Temporally Aligned Sensitive Health Information · medRxiv · 2026
• Speech Synthesis from Electrocorticography during Imagined Speech Using a Transformer-Based Decoder and a Pretrained Vocoder · bioRxiv · 2026
• Detecting Audio-Text Decontextualization through Entailment and Semantic Analysis · 2026
• Real-Time Voice Cloning and Streaming System · Zenodo (CERN European Organization for Nuclear Research) · 2026
• Optimizing ASR Models through Contrastive Learning-Based Audio Filtering · International Scientific Journal of Engineering and Management · 2026
• Enhancing continuous speech recognition with CapsNet and WaveRNN: a transfer learning approach · International Journal of Machine Learning and Cybernetics · 2026
• JODAL: Joint Domain Adversarial Learning for TTS-Augmented Automatic Speech Recognition · Mathematics · 2026
• Comparative Analysis of Japanese Speech: Applying Dynamic Time Warping and Precise Word Segmentation for Pronunciation Assessment · International Journal of Engineering and Technology Innovation · 2026
• HNQSM: A Novel Hybrid Neural-Quantum Approach for Advanced Speaker Verification and Identification · Arabian Journal for Science and Engineering · 2026

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