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Open research questions in Speech Recognition and Synthesis

27 unresolved questions extracted from the limitations and future-work sections of 393 Speech Recognition and Synthesis papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Self-supervised learning (SSL) models are widely used as feature extractors for state-of-the-art audio deepfake detection, but it remains unclear how to directly and quantitatively connect what SSL models capture to detection decisions.

    Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models · 2026

  • SSL speech models capture rich phonetic, prosodic, and acoustic patterns from raw audio, yet how they encode articulatory information across diverse languages remains unclear.

    How Bilingual Are SSL Speech Models? Cross-Lingual Probing of Articulatory Encoding with Finnish and Russian EMA · 2026

  • One underexplored phenomenon is consonant cluster reduction (CCR) in African American English (AAE), a widespread phonological process and a source of automatic speech recognition (ASR) disparity.

    Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English · 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

  • , 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

  • In this study, we develop effective noise-aware speech enhancement framework to improve speech quality and intelligibility of speech coding systems in noisy environ- ments. A comprehensive analysis shows no individual enhancement method performed consistently well across all noise types and SNR levels. Experimental results showed that the MCRA, Hirsch’s WSA, and Doblinger’s SMA offers distinct advantages depending on the noise condition. Also, proposed adaptive framework significantly improves both speech quality and intelligibility compared to fixed enhancement strategies. 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 pre- defined set of enhancement algorithms. In future work, noise classification performance will be further enhanced by exploring alternative acoustic features and advanced deep learning models. Additionally, the proposed noise-aware enhancement strategy will be extended to integrate with neural speech coding frameworks, followed by a comparative evaluation with recent neural codecs.

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

  • In addition, few studies have explored the intrinsic characteristics of audio adversarial examples or how these characteristics can be leveraged for robust detection.

    CAFAD: common acoustic features for adversarial audio detection · 2026 · DOI

  • We show this affirmation-only evaluation hides a key limitation: these models fail to encode negated sound concepts, mapping affirmative and negated captions to nearly identical representations.

    The Sound of Absence: Audio-Language Embedding Models Struggle with Negation · 2026

  • Current approaches are limited by the scale and ethological validity of input data; applications requiring large, rare, or naturalistic samples in particular would benefit from the ability to infer neural coding from incidental everyday speech.

    Estimation of neuronal tuning for word meaning from passively recorded naturalistic speech · 2026 · DOI

  • Spoofed speech detection is increasingly challenged by realistic synthesis, voice conversion, and replay attacks, with cross-dataset generalization remaining a major limitation.

    Robust Spoofed Speech Detection via Temporal Pyramid Modeling · 2026

  • Theoretically, directly modeling raw waveforms circumvents these issues; however, this direction remains underexplored and is often deemed difficult due to the extremely long sequence length of audio signals.

    WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling · 2026

  • Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produce overconfident predictions.

    Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing · 2026

  • Overall, these findings demonstrate the effectiveness and robustness of masked multimodal integration for silent speech synthesis, although adaptation to laryngectomized speakers remains an open research challenge.

    Cross-Modal Masking for Robust Silent Speech Synthesis Using sEMG and Lipreading · 2026

  • Introduced WaveNet conditioned on mel-spectrograms for natural, expressive speech synthesis, laying the foundation for modern neural TTS systems.

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

  • Limitations: 1) The major limitation of the model is that the representa- tions, while encoding the semantic aspects, compromise on encoding the non-semantic aspects of the speech signal.

    Representation Learning With Hidden Unit Clustering for Low Resource Speech Applications · 2024 · DOI

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27 open questions have been extracted from the limitations and future-work passages of 393 Speech Recognition and Synthesis papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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