High-resolution multipath estimation in wideband multiple-input multiple-output (MIMO) channels requires repeated searches over angle of arrival, angle of departure, and propagation distance. This paper presents neural network-assisted CLEAN (NN-CLEAN), which replaces the multidimensional search at each extraction step with a multi-head classifier while retaining least-squares (LS) estimation of the complex path gain, parametric reconstruction, and residual cancellation. The same classifier is applied to successive residuals, allowing the extraction depth to vary without changing its output structure. With the number of iterations prescribed by the reference multipath-component (MPC) count, off-grid synthetic tests show that NN-CLEAN slightly exceeds grid-search CLEAN (GS-CLEAN) in path accuracy for one and two MPCs, although its accuracy declines more rapidly at larger path counts. For complete three-MPC extraction at a batch size of eight, NN-CLEAN achieves an approximately $27\times$ speedup and a $52$-fold reduction in peak GPU memory relative to GS-CLEAN. Applied without retraining to two-MPC line-of-sight (LOS) and non-line-of-sight (NLOS) Sionna ray-tracing channels within a coded MIMO link, NN-CLEAN substantially reduces channel-reconstruction mean-squared error (MSE) and bit error rate (BER) relative to interpolated LS channel estimation. GS-CLEAN retains an accuracy advantage on these channels, indicating the estimation cost associated with the computational savings.
Text-to-speech systems built from a language model and a flow-matching mel decoder now run the decoder with two to four Euler steps instead of the official ten or more. At two steps the word error rate (WER) is unchanged, but speaker similarity (SIM) drops by one to six points. This paper shows that most of this loss comes from classifier-free guidance on the first step. At pure noise the network can only return the conditional mean, so guidance shifts every trajectory by nearly the same vector. Later steps do not remove this shift, and its size grows with the length of the first step, so it is negligible with ten or more steps and accounts for most of the loss with two. Two training-free samplers are derived from this analysis. First-step unguided sampling (FSU) leaves the first step unguided and recovers $84$--$91\%$ of the two-step SIM loss on CosyVoice~2/3 with no significant WER increase. FSU-Mid replaces the two Euler steps by one midpoint step with an unguided first evaluation, recovers $77$--$85\%$ on IndexTTS-2 and matches the ten-step teacher on CosyVoice~2 and GLM-TTS. Both use three network function evaluations, one fewer than the official two-step sampler, and FSU-Mid gives the highest mean recovery among eight published guidance rules re-run at two steps on four decoders.
JuneYoung Park, Yuri Hong, Seongwan Kim, Jaeho Lee
原文摘要
On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints, typically 6–12GB shared across all workloads. Existing approaches force a trade-off between exact gradients with high memory (MeBP) and low memory with noisy estimates (MeZO). We propose Memory-efficient Structured Backpropagation (MeSP), which bridges this gap by manually deriving backward passes that exploit LoRA’s low-rank structure. Our key insight is that the intermediate projection h = xA can be recomputed during backward at minimal cost since rank r \ll d_{in}, eliminating the need to store it. MeSP achieves 49% average memory reduction compared to MeBP on Qwen2.5 models (0.5B–3B) while computing mathematically identical gradients. Our analysis also reveals that MeZO’s gradient estimates show near-zero correlation with true gradients (cosine similarity \approx0.001), explaining its slow convergence. MeSP reduces peak memory from 361MB to 136MB for Qwen2.5-0.5B, enabling fine-tuning scenarios previously infeasible on memory-constrained devices.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表
Hyunjong Ok, Suho Yoo KAIST, Jaeho Lee Pohang University of Science and Technology
原文摘要
Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses.However, these systems struggle with end-turn detection (ETD)—the ability to distinguish between user turn completion and hesitation. This limitation often leads to premature or delayed responses, disrupting the flow of spoken conversations.In this paper, we introduce the OpenETD Dataset, the first public dataset for end-turn detection. The OpenETD dataset consists of both synthetic speech data generated with text-to-speech models and real-world speech data collected from web sources. We also propose SpeculativeETD, a novel collaborative inference framework that balances efficiency and accuracy to improve real-time ETD in resource-constrained environments. Our approach jointly employs a lightweight GRU-based model, which rapidly detects the non-speaking units in real-time on local devices, and a high-performance Wav2vec-based model running on the server to make a more challenging classification of distinguishing turn ends from mere pauses. Experiments demonstrate that the proposed SpeculativeETD significantly improves ETD accuracy while keeping the required computations low.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表