Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.38000

QK-GCC: Learnable Query-Key Spectral Matching for Robust Time Delay Estimation

Jinkai Zhang·Weiye Chen·Yue Huang·Xiaotong Tu·Xinghao Ding
View original source ↗Read source document ↗Export metadata ↓

Abstract

Time delay estimation (TDE) is a fundamental component of microphone-array sound source localization. Generalized cross-correlation (GCC) is widely used because it is efficient and interpretable, but its handcrafted spectral matching and predefined frequency weighting are vulnerable to noise and reverberation. Existing neural GCC variants mainly improve robustness by enhancing input signals or modeling GCC responses, while the cross-channel spectral matching step itself remains handcrafted. We propose QK-GCC, a learnable GCC-like framework that replaces handcrafted weighted spectral matching in GCC with Query-Key matching between two microphone signals. The two microphone signals are encoded as magnitude-phase frequency tokens and mapped to Query and Key representations, respectively, enabling frequency reliability learning and local spectral evidence aggregation for delay estimation. Experiments in simulated reverberant rooms across diverse SNR and reverberation conditions show that QK-GCC improves TDE accuracy over GCC-PHAT and learning-based GCC variants, while remaining lightweight and generalizing to unseen source types. The code is available at https://github.com/zhangjinkai33-ui/QK-GCC.

Explore related subjects

eess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jinkai Zhang, Weiye Chen, Yue Huang, Xiaotong Tu, Xinghao Ding. 2026-09-29. QK-GCC: Learnable Query-Key Spectral Matching for Robust Time Delay Estimation. https://arxiv.org/abs/2609.38000

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Bootstrapping Audiovisual Speech Recognition in Zero-AV-Resource Scenarios

Audiovisual speech recognition (AVSR) combines acoustic and visual cues to improve transcription robustness under challenging conditions but remains out of reach for most under-resourced languages due to the lack of labeled video corpora for training. Synthetic visual data have been shown to be an effective augmentation strategy for addressing AV data scarcity. However, a more challenging scenario arises for languages such as Catalan, where no real audiovisual data are available for training. In this study, we investigate whether AVSR can be bootstrapped in such a zero-AV-resource setting, using synthetic visual data as the sole source of visual supervision. We synthesize over 700 hours of talking-head video and fine-tune a pre-trained AV-HuBERT model. On a manually annotated Catalan benchmark, our model achieves near state-of-the-art (SOTA) performance with much fewer parameters and training data than SOTA ASR systems such as Whisper-large-v3, outperforms an identically trained audio-only baseline, and preserves multimodal advantages under acoustic degradation. Scalable synthetic video thus offers a viable substitute for real recordings in zero-AV-resource AVSR.

eess.AS↗
arXiv2026

Teacher-Free Self-Distilled Consistency Trajectory Learning for Fast Speech Enhancement

Consistency trajectory models offer a route to fast, high- quality speech enhancement, collapsing the many reverse steps of diffusion-based enhancers into a handful. When instantiated on a Schrödinger bridge (SB), which pins the generative process to fixed clean and noisy endpoints, exist- ing consistency-trajectory enhancers (SBCTMs) still require a pretrained teacher to supply trajectory supervision, which raises training cost and ties the final quality to that of the teacher. We propose a teacher-free, self-distilled consistency- trajectory framework that removes the external teacher result- ing in a 5X reduction in per epoch training time. Our model is trained with a three-stage curriculum of clean speech pre- diction, a self-distilled shortcut objective, and perceptual fine-tuning with a multi-resolution short-time Fourier trans- form (MR-STFT) loss. Using the same NCSN++ backbone as SBCTM, our model attains a wide-band PESQ of 3.01, ES- TOI 0.87, and SI-SDR 19.07 dB on VoiceBank+DEMAND compared to 3.57, 0.87 and 12.8 dB for the teacher based model. Further, we find that a geometric schedule at low reverse step count maximizes perceptual quality, while a higher-step uniform schedule favors signal fidelity.

eess.AS↗
arXiv2026

Pruned CTC for Memory-Efficient Large-Vocabulary ASR Training

Connectionist temporal classification (CTC) naturally supports offline and streaming speech recognition with utterance-level supervision, but conventional implementations materialize frame-by-vocabulary activations in memory, making CTC training with native LLM vocabularies prohibitively memory-intensive. A key observation is that every valid CTC alignment uses only target tokens and blank, and their union across a batch typically forms a small subset of the full vocabulary. We introduce Pruned CTC, which restricts alignment computation to this subset while retaining full-vocabulary normalization. We prove that this vocabulary reduction is exactly equivalent to full-vocabulary CTC in loss and gradients. Head-and-loss activation memory no longer scales linearly with vocabulary size. We further apply finite-beam alignment pruning. Building on Pruned CTC, we develop LLM-CTC, which adapts pretrained LLMs for non-autoregressive ASR while retaining causal attention and native vocabularies, and extend it to bounded-history streaming, avoiding chunk-level speech--text alignments. Experiments show that, with Zipformer-M encoder and 180K vocabulary, Pruned CTC reduces full-step memory by 5.1$\times$ with only 17% step-time overhead. Across three corpora, it matches standard CTC accuracy. On GigaSpeech, across six Qwen3 model sizes from 0.6B to 32B, LLM-CTC remains within 7% relative WER of LLM-CE with 7 to 10$\times$ faster recognition; when fine-tuning Qwen3-ASR for bounded-history streaming, LLM-CTC remains within 3% relative WER of matched offline models on the test set. Together, these results establish Pruned CTC as a scalable sequence objective for native-vocabulary LLM ASR across offline and streaming settings.

eess.AS↗