The long-drawn tussle between man and machine has made another breakthrough. Using snippets of voices, Chinese Technology Leader Baidu's'Deep Voice' can generate new speech, accents, and tones in only 3.7 seconds in comparison to the 30 minutes of audio the company's voice cloning tool required a year back. This demonstrates the accelerating advances, the technology to produce artificial voices, has made in such a short span of time. Also, it is indicative of the capabilities getting stronger and becoming more realistic with time, which may lead to abuse of the technology. As is true for all artificial intelligence algorithms, the more data is fed to the voice cloning tools such as Deep Voice to train with, the more realistic the results they produce.

It takes just 3.7 seconds of audio to clone a voice. This impressive--and a bit alarming--feat was announced by Chinese tech giant Baidu. A year ago, the company's voice cloning tool called Deep Voice required 30 minutes of audio to do the same. This illustrates just how fast the technology to create artificial voices is accelerating. In just a short time, the capabilities of AI voice generation have expanded and become more realistic which makes it easier for the technology to be misused.

It takes just 3.7 seconds of audio to clone a voice. This impressive--and a bit alarming--feat was announced by Chinese tech giant Baidu. A year ago, the company's voice cloning tool called Deep Voice required 30 minutes of audio to do the same. This illustrates just how fast the technology to create artificial voices is accelerating. In just a short time, the capabilities of AI voice generation have expanded and become more realistic which makes it easier for the technology to be misused.

With only a few seconds of audio, the'Deep Voice' software developed by China's Baidu is able to clone a human voice - raising fears about the security of biometrics. Baidu has been working on Deep Voice for over a year, and had already managed to reproduce speaker identities with about half an hour of training data. With new developments, it has lowered that time to 3.7 seconds. A believable, if low-quality, false voice can now be produced from a only single sentence of speech. Of course, more training leads to higher-quality results, especially if there is more than one sample to learn from.

Chinese internet search giant Baidu has developed an AI system that can clone an individual's voice! An year in the making, the text to speech system, called Deep Voice, can generate synthetic human voices using deep neural networks. According to the information shared by Baidu Research, they claim that it takes their trained model just three seconds to replicate and output a person's voice. Baidu's research team used voice cloning techniques to develop the AI system which they expect will have noteworthy applications in personalizing human-machine interface. Both Speaker Adaptation and Speaker Encoding (requiring minimal audio) provide quality performance and can be integrated in the Deep Voice model along with speaker embeddings without having to compromise the quality of the source audio. You can check out some audio samples provided by Baidu's Research team which consist of original and synthesized voices.

Chinese AI titan Baidu earlier this month announced its Deep Voice AI had learned some new tricks. Not only can it accurately clone an individual voice faster than ever, but now it knows how to make a British man sound like an American woman. You can insert your own joke here. The Baidu Deep Voice research team unveiled its novel AI capable of cloning a human voice with just 30 minutes of training material last year. And since then it's gotten much better at it: Deep Voice can do the same job with just a few seconds worth of audio now. Or it can change a human male voice into a female.

Baidu's research arm announced yesterday that its 2017 text-to-speech (TTS) system Deep Voice has learned how to imitate a person's voice using a mere three seconds of voice sample data. The technique, known as voice cloning, could be used to personalize virtual assistants such as Apple's Siri, Google Assistant, Amazon Alexa; and Baidu's Mandarin virtual assistant platform DuerOS, which supports 50 million devices in China with human-machine conversational interfaces. In healthcare, voice cloning has helped patients who lost their voices by building a duplicate. Voice cloning may even find traction in the entertainment industry and in social media as a tool for satirists. Baidu researchers implemented two approaches: speaker adaption and speaker encoding.

Google's WaveNet can also synthesize realistic human speech, but it's quite computationally demanding and hard to use for real-world applications at this point. Baidu says it solved WaveNet's problem by using deep-learning techniques to convert text to phenomes, the smallest unit of speech. It then turns those phonemes into sounds using its speech synthesis network. The system converts the word "hello," for instance, into "(silence HH), (HH, EH), (EH, L), (L, OW), (OW, silence)" before the speech network pronounces it. Both steps rely on deep learning and don't need human input.

Text-to-speech systems are familiar in the modern world in navigation apps, talking clocks, telephone answering systems, and so on. Traditionally these have been created by recording a large database of speech from a single individual and then recombining the utterances to make new phrases. The problem with these systems is that it is difficult to switch to a new speaker or change the emphasis in their words without recording an entirely new database. So computer scientists have been working on another approach. Their goal is to synthesize speech in real time from scratch as it is required.

Baidu Research presents Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. The biggest obstacle to building such a system thus far has been the speed of audio synthesis – previous approaches have taken minutes or hours to generate only a few seconds of speech. We solve this challenge and show that we can do audio synthesis in real-time, which amounts to an up to 400X speedup over previous WaveNet inference implementations. Synthesizing artificial human speech from text, commonly known as text-to-speech (TTS), is an essential component in many applications such as speech-enabled devices, navigation systems, and accessibility for the visually-impaired. Fundamentally, it allows human-technology interaction without requiring visual interfaces.