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Conspiracy Theorists Think Trump's Speech Paves the Path to the Insurrection Act Election deniers have spent years promoting the idea that the 2020 election was stolen. They believe Donald Trump's speech finally proves them right. President Donald Trump's much-hyped speech promising huge revelations about interference in the 2020 election failed to deliver. On Thursday night, the president made sweeping claims about interference by China and cover-ups by the " deep state," and repeated debunked claims about noncitizens voting . He pointed to a document drop on the White House website as proof, though the files didn't contain evidence to back up his assertions.

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Trump alleges'shocking vulnerabilities' in US election security ahead of midterms US President Donald Trump has delivered a primetime address in which he accused China of interfering in the 2020 election and alleged shocking vulnerabilities in American voting systems. Trump, who spoke from the White House on Thursday, has repeatedly made unsubstantiated claims about voter fraud and foreign meddling in the 2020 election which he lost to Joe Biden. In the half-hour speech, delivered three months before the midterm elections, he said he had declassified hundreds of intelligence files which supported his claims that Beijing had tried to sway the election in Biden's favour. The US intelligence community has previously concluded China did not interfere in the 2020 election. Trump spoke in front of several members of his top team as he gave his address, but journalists were unable to put questions to the president.

Anthony Albanese is set to announce the creation of a new office of AI within his department. Anthony Albanese is set to announce the creation of a new office of AI within his department. Anthony Albanese says the federal government will introduce faster approval processes for AI projects, including datacentres, across Australia, seeking to shore up investor certainty and maintain community confidence in the rapidly advancing technology . Announcing the creation of a new office of AI to be established within his department in a major speech on Wednesday, the prime minister will declare Australia is set to become the first country in the world to bring the economic, social, national security and environmental issues stemming from AI into a single, national framework. Sign up for the Breaking News Australia email "Getting this right will enhance our appeal to international investors, by delivering greater clarity and speed for approvals, and a streamlined process for verifying compliance," Albanese will tell an event .

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Speech is a fundamental form of human communication, and speech perception constitutes the initial stage of language comprehension. Although brain-to-speech interface technologies have made significant progress in recent years, most existing studies focus on neural decoding during speech production. Such approaches heavily rely on articulatory motor regions, rendering them unsuitable for individuals with speech motor impairments, such as those with aphasia or locked-in syndrome. To address this limitation, we construct and release NeuroListen, the first publicly available stereo-electroencephalography (sEEG) dataset specifically designed for auditory reconstruction. It contains over 10 hours of neuralspeech paired recordings from 5 clinical participants, covering a wide range of semantic categories. Building on this dataset, we propose HyperSpeech, a multi-band neural decoding framework that employs dynamic spatio-temporal hypergraph neural networks to capture high-order dependencies across frequency, spatial, and temporal dimensions. Experimental results demonstrate that HyperSpeech significantly outperforms existing methods across multiple objective speech quality metrics, and achieves superior performance in human subjective evaluations, validating its effectiveness and advancement. This study provides a dedicated dataset and modeling framework for auditory speech decoding, offering foundations for neural language processing and assistive communication systems.

Generating natural-sounding, multi-speaker dialogue is crucial for applications such as podcast creation, virtual agents, and multimedia content generation. However, existing systems struggle to maintain speaker consistency, model overlapping speech, and synthesize coherent conversations efficiently. In this paper, we introduce CoVoMix2, a fully non-autoregressive framework for zero-shot multi-talker dialogue generation. CoVoMix2 directly predicts mel-spectrograms from multistream transcriptions using a flow-matching-based generative model, eliminating the reliance on intermediate token representations. To better capture realistic conversational dynamics, we propose transcription-level speaker disentanglement, sentence-level alignment, and prompt-level random masking strategies. Our approach achieves state-of-the-art performance, outperforming strong baselines like MoonCast and Sesame in speech quality, speaker consistency, and inference speed. Notably, CoVoMix2 operates without requiring transcriptions for the prompt and supports controllable dialogue generation, including overlapping speech and precise timing control, demonstrating strong generalizability to real-world speech generation scenarios. Audio samples are available 3.

Mixture-of-Experts (MoE) models have become popular in machine learning, boosting performance by partitioning tasks across multiple experts. However, the need for several experts often results in high computational costs, limiting their application on resource-constrained devices with stringent real-time requirements, such as cochlear implants (CIs). We introduce the Omni-Expert (OE) - a simple and efficient solution that leverages feature transformations to achieve the'divideand-conquer' functionality of a full MoE ensemble in a single expert model. We demonstrate the effectiveness of the OE using phoneme-specific time-frequency masking for speech dereverberation in a CI. Empirical results show that the OE delivers statistically significant improvements in objective intelligibility measures of CI vocoded speech at different levels of reverberation across various speech datasets at a much reduced computational cost relative to a counterpart MoE.