Speech


Xiaomi's Xiao AI assistant passes 49.9 million users

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Today marked the kickoff of Xiaomi's annual Mi Developer conference in Beijing, and the tech giant wasted no time in announcing updates across its AI portfolio. It took the wraps off the latest release of Mobile AI Compute Engine (MACE), its open source machine learning framework, and it demoed an improved version of its Xiao AI voice assistant (Xiao AI 3.0). Xiao AI, which Xiaomi says is used by 49.9 million users each month, will soon support multi-turn conversations à la Alexa Conversations and Google's Continued Conversation. This will be enabled on select phones, including the Xiaomi Mi 9 Pro and the Xiaomi Mi 9 via a software update, and it will allow for interruptions of the assistant at any time with new requests or commands. Xiao AI 3.0 also boasts improved voice shortcut functionality and a voice reply feature that will let users respond to incoming calls with transcribed text messages.


3 Ways Machine Learning Algorithms Are Improving Our Day-To-Day Life - Latest, Trending Automation News

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Ever since the advent of mankind, humans have tried to make living easier on the face of the earth. It was because of this search for ease that has led us to the three industrial revolutions. Today, we are approaching fast towards fourth industrial revolution and it is all because of Artificial Intelligence and Machine Learning. Machine Learning algorithms have enabled the invention and development of intelligent software. These intelligent software, machines and robots have made both business and domestic life better.


How To Connect A Bot to Microsoft Speech

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Through Bot Libre, your bots may use Microsoft Speech for text-to-speech. This "How To" will give you a step by step process to connect your bot with Microsoft Speech. First you must create a bot that you want to connect to Microsoft Speech, or you can use one of your existing bots. To create a bot, follow the instructions here: How to create your own chat bot in 10 clicks. Click the "Free Account" button to create an account, or sign in if you have an existing one.


Ray Kurzweil (USA) at Ci2019 - The Future of Intelligence, Artificial and Natural

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Called "the restless genius" by The Wall Street Journal and "the ultimate thinking machine" by Forbes magazine, he was selected as one of the top entrepreneurs by Inc. magazine, which described him as the "rightful heir to Thomas Edison." PBS selected him as one of the "sixteen revolutionaries who made America." Ray was the principal inventor of the first CCD flat-bed scanner, the first omni-font optical character recognition, the first print-to-speech reading machine for the blind, the first text-to-speech synthesizer, the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition. Among Ray's many honors, he received a Grammy Award for outstanding achievements in music technology; he is the recipient of the National Medal of Technology, was inducted into the National Inventors Hall of Fame, holds twenty-one honorary Doctorates, and honors from three U.S. presidents. Ray has written five national best-selling books, including New York Times best sellers The Singularity Is Near (2005) and How To Create A Mind (2012). He is Co-Founder and Chancellor of Singularity University and a Director of Engineering at Google heading up a team developing machine intelligence and natural language understanding.


Senior Machine Learning Engineer (Voice Software) ai-jobs.net

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This is an exciting opportunity to shape the future of voice interaction at Dyson. Working within a small team you will be responsible for building the software framework to enable rapid prototyping and development of voice control and dialogue systems. Your goal will be to implement the functionality of the latest API's for Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) across embedded and cloud platforms. You will use your deep understanding and experience to determine the software and hardware architecture for voice control applications on our next generation products.


6 Ways Speech Synthesis Is Being Powered By Deep Learning

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This model was open sourced back in June 2019 as an implementation of the paper Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. This service is being offered by Resemble.ai. With this product, one can clone any voice and create dynamic, iterable, and unique voice content. Users input a short voice sample and the model -- trained only during playback time -- can immediately deliver text-to-speech utterances in the style of the sampled voice. Bengaluru's Deepsync offers an Augmented Intelligence that learns the way you speak.


AI for Accessibility

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According to the World Health Organization, 1 billion people on Earth have some form of disability. It's not surprising, then, that Microsoft's AI for Good campaign supports efforts that drive accessibility, empowering people to achieve more, regardless of their level of ability. AI for Good is a $50 million commitment from Microsoft to enable innovators to create solutions that leverage artificial intelligence (AI) technologies. The support includes use of Microsoft's Azure cloud and AI tools. AI can serve as the'brains' behind tools that enhance independence and productivity for people who have disabilities.


Intel's AI brains will give future laptops a new speed boost

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Intel's Nervana NNP-I chips are designed to be crammed into data centers for AI tasks like translating text or analyzing photos. It may not be obvious, but you're almost certainly using AI every day. Artificial intelligence-boosting hardware in your phone enables voice recognition and spots your friends in photos. In the cloud, it delivers search results and weeds out spam email. Next up for dedicated AI hardware will be your laptop, Intel expects.


Audio samples from "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis"

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Abstract: We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.


LightCommands Audio Injection Attacks Threaten Voice Assistants

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Researchers have come up with a new attack strategy against smart assistants. These attacks threaten all devices featuring voice assistants. Dubbed as'LightCommands', these attacks enable a potential attacker to inject voice commands to the devices and take control of them. Researchers have developed new attacks that allow meddling with smart assistants. These attacks named'LightCommands' allow injecting audio signals to voice assistants.