End-to-end speech summarization on long recordings is challenging because of the high computational cost. Block-wise Adaptation for Speech Summarization (BASS) summarizes arbitrarily long sequences by sequentially processing abutting chunks of audio. Despite the benefits of BASS, it has higher compute time due to sequential processing of all blocks, regardless of whether they are relevant to the final summary. In this paper, we propose R-BASS, a new relevance-aware block-wise adaptation method. First, we introduce two approaches to automatically estimate block relevance based on lexical and semantic similarity between the block-level transcript and the summary. Experiments on the How2 dataset show that using ground truth relevance during inference improves efficiency by 63.9 % by dropping irrelevant blocks. Finally, we incorporate relevance scores into training using a novel relevance loss and relevance predictor, and the proposed R-BASS model makes it possible to drop 86.3 % of the blocks while retaining comparable performance, resulting in a 2.2x speedup over BASS.

Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback remains a significant impediment. To address this challenge, in this paper, we propose a novel self-supervised framework **AutoPRM** that efficiently enhances the fine-tuning of LLMs for intricate reasoning challenges. Specifically, **AutoPRM** first decomposes complex problems into more manageable subquestions with a controllable granularity switch, then sequentially apply reinforcement learning to iteratively improve the subquestion solver. Additionally, we propose context-guided decoding to avoid reward tampering and guide the subquestion solver towards the solution of the holistic problem. Extensive experiments show that **AutoPRM** significantly improves performance on mathematical and commonsense reasoning tasks over SOTA. More encouragingly, **AutoPRM** can be easily integrated with other orthogonal reasoning pipelines.

We present the results of Shared Task “Dialect to MSA Translation”, which tackles challenges posed by the diverse Arabic dialects in machine translation. Covering Gulf, Egyptian, Levantine, Iraqi and Maghrebi dialects, the task offers 1001 sentences in both MSA and dialects for fine-tuning, alongside 1888 blind test sentences. Leveraging GPT-3.5, a state-of-the-art language model, our method achieved the a BLEU score of 29.61. This endeavor holds significant implications for Neural Machine Translation (NMT) systems targeting low-resource langu ages with linguistic variation. Additionally, negative experiments involving fine-tuning AraT5 and No Language Left Behind (NLLB) using the MADAR Dataset resulted in BLEU scores of 10.41 and 11.96, respectively. Future directions include expanding the dataset to incorporate more Arabic dialects and exploring alternative NMT architectures to further enhance translation capabilities.

Linguistic communication is prevalent in Human-Computer Interaction (HCI). Speech (spoken language) serves as a convenient yet potentially ambiguous form due to noise and accents, exposing a gap compared to text. In this study, we investigate the prominent HCI task, Referring Video Object Segmentation (R-VOS), which aims to segment and track objects using linguistic references. While text input is well-investigated, speech input is under-explored. Our objective is to bridge the gap between speech and text, enabling the adaptation of existing text-input R-VOS models to accommodate noisy speech input effectively. Specifically, we propose a method to align the semantic spaces between speech and text by incorporating two key modules: 1) Noise-Aware Semantic Adjustment (NSA) for clear semantics extraction from noisy speech; and 2) Semantic Jitter Suppression (SJS) enabling R-VOS models to tolerate noisy queries. Comprehensive experiments conducted on the challenging AVOS benchmarks reveal that our proposed method outperforms state-of-the-art approaches.

This paper introduces a novel approach for identifying the possible large language models (LLMs) involved in text generation. Instead of adding an additional classification layer to a base LM, we reframe the classification task as a next-token prediction task and directly fine-tune the base LM to perform it. We utilize the Text-to-Text Transfer Transformer (T5) model as the backbone for our experiments. We compared our approach to the more direct approach of utilizing hidden states for classification. Evaluation shows the exceptional performance of our method in the text classification task, highlighting its simplicity and efficiency. Furthermore, interpretability studies on the features extracted by our model reveal its ability to differentiate distinctive writing styles among various LLMs even in the absence of an explicit classifier. We also collected a dataset named OpenLLMText, containing approximately 340k text samples from human and LLMs, including GPT3.5, PaLM, LLaMA, and GPT2.

While Automatic Speech Recognition has been shown to be vulnerable to adversarial attacks, defenses against these attacks are still lagging. Existing, naive defenses can be partially broken with an adaptive attack. In classification tasks, the Randomized Smoothing paradigm has been shown to be effective at defending models. However, it is difficult to apply this paradigm to ASR tasks, due to their complexity and the sequential nature of their outputs. Our paper overcomes some of these challenges by leveraging speech-specific tools like enhancement and ROVER voting to design an ASR model that is robust to perturbations. We apply adaptive versions of state-of-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion.

The automation of the diagnosis and monitoring of speech affecting diseases in real life situations, such as Depression or Parkinson’s disease, depends on the existence of rich and large datasets that resemble real life conditions, such as those collected from in-the-wild multimedia repositories like YouTube. However, the cost of manually labeling these large datasets can be prohibitive. In this work, we propose to overcome this problem by automating the annotation process, without any requirements for human intervention. We formulate the annotation problem as a Multiple Instance Learning (MIL) problem, and propose a novel solution that is based on end-to-end differentiable neural networks. Our solution has the additional advantage of generalizing the MIL framework to more scenarios where the data is stil organized in bags but does not meet the MIL bag label conditions. We demonstrate the performance of the proposed method in labeling the in-the-Wild Speech Medical (WSM) Corpus, using simple textual cues extracted from videos and their metadata. Furthermore we show what is the contribution of each type of textual cues for the final model performance, as well as study the influence of the size of the bags of instances in determining the difficulty of the learning problem