Upgrading to one of the best smartwatches can be exciting, but it often means your old smartwatch is left with nothing to do. Unfortunately, it's at this point that users may toss their old smartwatch in a drawer and forget about it. But it doesn't have to be like that. Your old smartwatch is a versatile piece of hardware, so despite how it seems, that small device that's been gathering dust still has some pretty unexpected and interesting uses. With the right guidance, you can give your old smartwatch a new lease on life, or at the very least, you could sell it for some quick cash.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Are you going to remember what was said in this meeting? We may earn revenue from the products available on this page and participate in affiliate programs. Whether you work from home, have to schlep into an office, or both, chances are you spend a fair bit of time on video calls. For any job where you work with colleagues, clients, or contractors, video calls are par for the course now--and that's before you get into video meetings you might have in your spare time, for book clubs or family reunions.

Meta has introduced its first real-time audio model, Muse Voice Transcribe. The model can handle dictation and transcription for more than 20 speakers and can seamlessly handle multiple languages at once, Meta says. Meta CEO Mark Zuckerberg, who recently returned to X after three years of not posting on the platform, shared an example of the model's ability to handle multiple speakers and languages at once. In the video, the transcription is able to automatically distinguish between multiple speakers and switch between languages. It's even able to pick up on "code-switching" and transcribe sentences that use words from multiple languages.

Smartphones have long offered voice-to-text transcription, but the same can't be said for sign-language-to-text. That's changing today with the launch of the Pixel 11 family, which marks the debut of DeepMind's new sign-language-to-text (SL2T) model. Google has embedded the model inside of Gboard and Live Transcribe, where it will allow deaf and hard of hearing users to sign to their phone anywhere where they would have normally typed in the past. In practice, that will give those users a faster, more natural way to search the web, write messages and converse with Google's Gemini chatbot. "There is great diversity among deaf people in terms of their level of proficiency in signing, speaking, reading, and writing, so it is important to support access in all modalities," says DeepMind.

When you purchase through links in our articles, we may earn a small commission. Plaud Note Pro is the AI meeting gadget I didn't know I wanted Plaud Note Pro is a really smart AI tool for transcribing everything from face-to-face meetings to mobile calls and online video meetings. The charging cable isn't included as standard Doesn't work with headphones, and can be tricky with certain Android handsets (phone calls) Plaud Note Pro is a really smart and handy AI assistant that brings all your recordings -- physical and digital -- together in one place. Transcription and meeting summaries work well, and you can choose between different AI models, depending on which one best suits what you want to do with the recordings. There are plenty of AI services for transcription and meeting notes.

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.

There has been a surge of interest in assistive wearable agents: agents embodied in wearable form factors (e.g., smart glasses) who take assistive actions toward a user's goal/query (e.g. "Where did I leave my keys?"). In this work, we consider the important complementary problem of inferring that goal from multi-modal contextual observations. Solving this "goal inference" problem holds the promise of eliminating the effort needed to interact with such an agent. This work focuses on creating WAGIBench, a strong benchmark to measure progress in solving this problem using vision-language models (VLMs). Given the limited prior work in this area, we collected a novel dataset comprising 29 hours of multimodal data from 348 participants across 3,477 recordings, featuring ground-truth goals alongside accompanying visual, audio, digital, and longitudinal contextual observations. We validate that human performance exceeds model performance, achieving 93% multiple-choice accuracy compared with 84% for the best-performing VLM. Generative benchmark results that evaluate several families of modern vision-language models show that larger models perform significantly better on the task, yet remain far from practical usefulness, as they produce relevant goals only 55% of the time. Through a modality ablation, we show that models benefit from extra information in relevant modalities with minimal performance degradation from irrelevant modalities.

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 multi-stream 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.

Digital rights experts have raised concerns about the security of the data recorded by AI in psychiatrists' sessions. Digital rights experts have raised concerns about the security of the data recorded by AI in psychiatrists' sessions. Melbourne psychiatrist refuses new patients who don't consent to AI note-taking A Melbourne psychiatrist has refused new patients unless they agree to allow her to use an AI scribe to transcribe the conversations in their sessions. AI-driven note taking tools are becoming popular within the medical industry - with two in five general practitioners now using such scribes, according to the Royal Australian College of General Practitioners (RACGP). But there have also been concerns about the security of the data and how it might be used by the AI companies, along with the accuracy of the transcriptions.

The lack of publicly available high-quality and accurately labeled datasets has long been a major bottleneck for singing voice synthesis (SVS). To tackle this problem, we present M4Singer, a free-to-use Multi-style, Multi-singer Mandarin singing collection with elaborately annotated Musical scores as well as its benchmarks. Specifically, 1) we construct and release a large high-quality Chinese singing voice corpus, which is recorded by 20 professional singers, covering 700 Chinese pop songs as well as all the four SATB types (i.e., soprano, alto, tenor, and bass); 2) we take extensive efforts to manually compose the musical scores for each recorded song, which is necessary to the study of the prosody modeling for SVS. 3) To facilitate the use and demonstrate the quality of M4Singer, we conduct four different benchmark experiments: score-based SVS, controllable singing voice (CSV), singing voice conversion (SVC) and automatic music transcription (AMT). Audio samples can be found at http://m4singer.github.io.