Recent end-to-end robotic manipulation research increasingly adopts architectures inspired by large language models to enable robust manipulation. However, a critical challenge arises from severe distribution shifts between robotic action data, primarily due to substantial numerical variations in action commands across diverse robotic platforms and tasks, hindering the effective transfer of pretrained knowledge. To address this limitation, we propose a semantically grounded linguistic representation to normalize actions for efficient pretraining. Unlike conventional discretized action representations that are sensitive to numerical scales, the motion representation specifically disregards numeric scale effects, emphasizing directionality instead. This abstraction mitigates distribution shifts, yielding a more generalizable pretraining representation. Moreover, using the motion representation narrows the feature distance between action tokens and standard vocabulary tokens, mitigating modality gaps. Multi-task experiments on two benchmarks demonstrate that the proposed method significantly improves generalization performance and transferability in robotic manipulation tasks.

The Multimodal Information based Speech Processing (MISP) 2022 challenge aimed to enhance speech processing performance in harsh acoustic environments by leveraging additional modalities such as video or text. The challenge included two tracks: audio-visual speaker diarization (AVSD) and audio-visual diarization and recognition (AVDR). The training material was based on previous MISP 2021 recordings, but we have accurately synchronized audio and visual data. Additionally, a new evaluation set was provided. This paper gives an overview of the challenge setup, presents the results, and summarizes the effective techniques employed by the participants. We also analyze the current technical challenges and suggest directions for future research in AVSD and AVDR.

The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research into wake-up words, speaker diarization, speech recognition, and other technologies. The MISP2022 challenge has two tracks: 1) audio-visual speaker diarization (AVSD), aiming to solve "who spoken when" using both audio and visual data; 2) a novel audio-visual diarization and recognition (AVDR) task that focuses on addressing "who spoken what when" with audio-visual speaker diarization results. Both tracks focus on the Chinese language, and use far-field audio and video in real home-tv scenarios: 2-6 people communicating each other with TV noise in the background. This paper introduces the dataset, track settings, and baselines of the MISP2022 challenge. Our analyses of experiments and examples indicate the good performance of AVDR baseline system, and the potential difficulties in this challenge due to, e.g., the far-field video quality, the presence of TV noise in the background, and the indistinguishable speakers.

In this paper we discuss the rational of the Multi-model Information based Speech Processing (MISP) Challenge, and provide a detailed description of the data recorded, the two evaluation tasks and the corresponding baselines, followed by a summary of submitted systems and evaluation results. The MISP Challenge aims at tack-ling speech processing tasks in different scenarios by introducing information about an additional modality (e.g., video, or text), which will hopefully lead to better environmental and speaker robustness in realistic applications. In the first MISP challenge, two bench-mark datasets recorded in a real-home TV room with two reproducible open-source baseline systems have been released to promote research in audio-visual wake word spotting (AVWWS) and audio-visual speech recognition (AVSR). To our knowledge, MISP is the first open evaluation challenge to tackle real-world issues of AVWWS and AVSR in the home TV scenario.

This paper presents our main contributions of acoustic modeling for multi-array multi-talker speech recognition in the CHiME-6 Challenge, exploring different strategies for acoustic data augmentation and neural network architectures. First, enhanced data from our front-end network preprocessing and spectral augmentation are investigated to be effective for improving speech recognition performance. Second, several neural network architectures are explored by different combinations of deep residual network (ResNet), factorized time delay neural network (TDNNF) and residual bidirectional long short-term memory (RBiLSTM). Finally, multiple acoustic models can be combined via minimum Bayes risk fusion. Compared with the official baseline acoustic model, the proposed solution can achieve a relatively word error rate reduction of 19% for the best single ASR system on the evaluation data, which is also one of main contributions to our top system for the Track 1 tasks of the CHiME-6 Challenge.

This paper proposes a lattice-based sequential discriminative training method to extract more discriminative bottleneck features. In our method, the bottleneck neural network is first trained with cross entropy criteria, and then only the weights of bottleneck layer are retrained with sequential criteria. If the outputs of the layer before bottleneck are treated as the raw features, the new method is an equivalent to a linear feature transformation algorithm. This linearity makes the optimization much easier than updating the whole neural network. Just like the fMPE and RDLT, the neural network is retrained with batch mode gradient descent, making the training to be easily implemented in parallel. Meanwhile, batch mode optimization can naturally deal with the indirect gradient to make the optimization more precise. Experimental results on a Mandarin transcription task and the Switchboard task have shown the effectiveness of the proposed method with the CER decreases from 12.2% to 11.3% and the WER from 16.1% to 15.0%, respectively.