Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user speech streams while generating responses. This simultaneous listening and speaking design enables real-time interaction and the agent can handle dynamic conversational behaviors like user barge-in. However, during the listening phase, existing systems keep the agent idle by repeatedly predicting the silence token, which departs from human behavior: we usually engage in lightweight thinking during conversation rather than remaining absent-minded. Inspired by this, we propose Chronological Thinking, an on-the-fly conversational thinking mechanism that aims to improve response quality in full-duplex SDLMs. Specifically, chronological thinking presents a paradigm shift from conventional LLM thinking approaches, such as Chain-of-Thought, purpose-built for streaming acoustic input. (1) Strictly causal: the agent reasons incrementally while listening, updating internal hypotheses only from past audio with no lookahead. (2) No additional latency: reasoning is amortized during the listening window; once the user stops speaking, the agent halts thinking and begins speaking without further delay. Experiments demonstrate the effectiveness of chronological thinking through both objective metrics and human evaluations show consistent improvements in response quality. Furthermore, chronological thinking robustly handles conversational dynamics and attains competitive performance on full-duplex interaction metrics.

Although automatic speech recognition (ASR) task has gained remarkable success by sequence-to-sequence models, there are two main mismatches between its training and testing that might lead to performance degradation: 1) The typically used cross-entropy criterion aims to maximize log-likelihood of the training data, while the performance is evaluated by word error rate (WER), not log-likelihood; 2) The teacher-forcing method leads to the dependence on ground truth during training, which means that model has never been exposed to its own prediction before testing. In this paper, we propose an optimization method called self-critical sequence training (SCST) to make the training procedure much closer to the testing phase. As a reinforcement learning (RL) based method, SCST utilizes a customized reward function to associate the training criterion and WER. Furthermore, it removes the reliance on teacher-forcing and harmonizes the model with respect to its inference procedure. We conducted experiments on both clean and noisy speech datasets, and the results show that the proposed SCST respectively achieves 8.7% and 7.8% relative improvements over the baseline in terms of WER.

Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in face of various practical environments. However, such plenty of in-domain data is not always available in the real-life world. In this paper, we propose a generative adversarial network to simulate noisy spectrum from the clean spectrum (SimuGAN), where only 10 minutes of unparalleled in-domain noisy speech data is required as labels. Furthermore, we also propose a dual-path speech recognition system to improve the robustness of the system under noisy conditions. Experimental results show that the proposed speech recognition system achieves 7.3% absolute improvement with simulated noisy data by Simu-GAN over the best baseline in terms of word error rate (WER).

Speech enhancement (SE) aims to suppress the additive noise from noisy speech signals to improve the speech’s perceptual quality and intelligibility. However, the over-suppression phenomenon in the enhanced speech might degrade the performance of downstream automatic speech recognition (ASR) task due to the missing latent information. To alleviate such problem, we propose an interactive feature fusion network (IFF-Net) for noise-robust speech recognition to learn complementary information from the enhanced feature and original noisy feature. Experimental results show that the proposed method achieves absolute word error rate (WER) reduction of 4.1% over the best baseline on RATS Channel-A corpus. Our further analysis indicates that the proposed IFF-Net can complement some missing information in the over-suppressed enhanced feature.

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. To tackle such challenges, we propose a progressive continual learning strategy for small-footprint spoken keyword spotting (PCL-KWS). Specifically, the proposed PCL-KWS framework introduces a network instantiator to generate the task-specific sub-networks for remembering previously learned keywords. As a result, the PCL-KWS approach incrementally learns new keywords without forgetting prior knowledge. Besides, the proposed keyword-aware network scaling mechanism of PCL-KWS constrains the growth of model parameters while achieving high performance. Experimental results show that after learning five new tasks sequentially, our proposed PCL-KWS approach archives the new state-of-the-art performance of 92.8% average accuracy for all the tasks on Google Speech Command dataset compared with other baselines.

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment.This problem will be more challenging if KWS models are further required for edge devices due to their limited memory.To alleviate such an issue, we propose a novel diversity-aware incremental learning method named Rainbow Keywords (RK).Specifically, the proposed RK approach introduces a diversity-aware sampler to select a diverse set from historical and incoming keywords by calculating classification uncertainty.As a result, the RK approach can incrementally learn new tasks without forgetting prior knowledge.Besides, the RK approach also proposes data augmentation and knowledge distillation loss function for efficient memory management on the edge device.Experimental results show that the proposed RK approach achieves 4.2% absolute improvement in terms of average accuracy over the best baseline on Google Speech Command dataset with less required memory.The scripts are available on GitHub 1 .

Neural speech enhancement degrades significantly in face of unseen noise. To address such mismatch, we propose to learn noise-agnostic feature representations by disentanglement learning, which removes the unspecified noise factor, while keeping the specified factors of variation associated with the clean speech. Specifically, a discriminator module is introduced to distinguish the type of noises, which is referred to as the disentangler. With the adversarial training strategy, a gradient reversal layer seeks to disentangle the noise factor and remove it from the feature representation. Experiment results show that the proposed approach achieves 5.8% and 5.2% relative improvements over the best baseline in terms of perceptual evaluation of the speech quality (PESQ) and segmental signal-to-noise ratio (SSNR), respectively. The ablation study indicates that the proposed disentangler module is also effective in other encoder-decoder-like structures.

In this paper, we study the time-domain neural network approach for speech bandwidth extension. We propose a network architecture, named multi-scale fusion neural network (MfNet), that gradually restores the low-frequency signal and predicts the high-frequency signal through the exchange of information across different scale representations. We propose a training scheme to optimize the network with a combination of perceptual loss and time-domain adversarial loss. Experiments show the proposed multi-scale fusion network consistently outperforms the competing methods in terms of perceptual evaluation of speech quality (PESQ), signal to distortion rate (SDR), signal to noise ratio (SNR), log-spectral distance (LSD) and word error rate (WER). More promisingly, the multi-scale fusion network requires only 10% of the parameters of the time-domain reference baseline.

Speech bandwidth extension aims to generate a wideband signal from a narrowband (low-band) input by predicting the missing high-frequency components. It is believed that the general knowledge about the speaker and phonetic content strengthens the prediction. In this paper, we propose to augment the low-band acoustic features with i-vector and phonetic posteriorgram (PPG), which represent speaker and phonetic content of the speech, respectively. We also propose a residual dual-path network (RDPN) as the core module to process the augmented features, which fully utilizes the utterance-level temporal continuity information and avoids gradient vanishing. Experiments show that the proposed method achieves 20.2% and 7.0% relative improvements over the best baseline in terms of log-spectral distortion (LSD) and signal-to-noise ratio (SNR), respectively. Furthermore, our method is 16 times more compact than the best baseline in terms of the number of parameters.

Bandwidth extension aims to reconstruct wideband speech signals from narrowband inputs to improve perceptual quality. Prior studies mostly perform bandwidth extension under the assumption that the narrowband signals are clean without noise. The use of such extension techniques is greatly limited in practice when signals are corrupted by noise. To alleviate such problem, we propose an end-to-end time-domain framework for noise-robust bandwidth extension, that jointly optimizes a mask-based speech enhancement and an ideal bandwidth extension module with multi-task learning. The proposed framework avoids decomposing the signals into magnitude and phase spectra, therefore, requires no phase estimation. Experimental results show that the proposed method achieves 14.3% and 15.8% relative improvements over the best baseline in terms of perceptual evaluation of speech quality (PESQ) and log-spectral distortion (LSD), respectively. Furthermore, our method is 3 times more compact than the best baseline in terms of the number of parameters.