Speech Synthesis


Google Creates A Text To Speech AI system Alike Human voice

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Google has plunged high towards its'AI-first' dream. The tech giant has attempted to develop a Text-to-speech system that has exactly human-like articulation. This AI system is called "Tacotron 2" that has the ability to give an AI-generated computer speech in a human-voice. Google researchers mentioned in the blog post that the new procedure does not utitilise complex linguistic and acoustic features as input. In place of it, they developed human-like speech from text using neural networks trained using only speech examples and corresponding text transcript.


Google's New Text-to-Speech AI Is so Good We Bet You Can't Tell It From a Real Human

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Can you tell the difference between AI-generated computer speech and a real, live human being? Maybe you've always thought you could. Maybe you're fond of Alexa and Siri but believe you would never confuse either of them with an actual woman. Things are about to get a lot more interesting. Google engineers have been hard at work creating a text-to-speech system called Tacotron 2. According to a paper they published this month, the system first creates a spectrogram of the text, a visual representation of how the speech should sound.


Google develops human-like text-to-speech artificial intelligence system

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In a major step towards its "AI first" dream, Google has developed a text-to-speech artificial intelligence (AI) system that will confuse you with its human-like articulation. The tech giant's text-to-speech system called "Tacotron 2" delivers an AI-generated computer speech that almost matches with the voice of humans, technology news website Inc.com reported. At Google I/O 2017 developers conference, company's Indian-origin CEO Sundar Pichai announced that the internet giant was shifting its focus from mobile-first to "AI first" and launched several products and features, including Google Lens, Smart Reply for Gmail and Google Assistant for iPhone. According to a paper published in arXiv.org, the system first creates a spectrogram of the text, a visual representation of how the speech should sound. That image is put through Google's existing WaveNet algorithm, which uses the image and brings AI closer than ever to indiscernibly mimicking human speech.


Flipboard on Flipboard

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Can you tell the difference between AI-generated computer speech and a real, live human being? Maybe you've always thought you could. Maybe you're fond of Alexa and Siri but believe you would never confuse either of them with an actual woman. Things are about to get a lot more interesting. Google engineers have been hard at work creating a text-to-speech system called Tacotron 2. According to a paper they published this month, the system first creates a spectrogram of the text, a visual representation of how the speech should sound.


Google's new text-to-speech system sounds convincingly human

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Get ready for the little person living inside your phone and speaker to sound a lot more life-like. Google believes it has reached a new milestone in the quest to make computer-generated speech indistinguishable from human speech with Tacotron 2, a system that trains neural networks to generate eerily natural-sounding speech from text, and they have the samples to prove it. In a research paper published earlier this month, though yet to be peer-reviewed, Google asserts that previous approaches to text-to-speech (TTS) systems have thus far failed to achieve a genuinely natural sound. Techniques such as concatenative synthesis, in which pre-recorded samples of speech are stitched together, and statistical parametric speech synthesis, Google says have been insufficient, explaining, "The audio produced by these systems often sounds muffled and unnatural compared to human speech." With Tacotron 2 (which is not the same as the world-ending super-weapon used by Lord Business), the company says it has incorporated ideas from its previous TTS systems, WaveNet and the first Tacotron, to reach a new level of fidelity.


High-fidelity speech synthesis with WaveNet DeepMind

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During training, the student network starts off in a random state. The generated waveform is then fed to the trained WaveNet model, which scores each sample, giving the student a signal to understand how far away it is from the teacher network's desired output. Over time, the student network can be tuned - via backpropagation - to learn what sounds it should produce. Put another way, both the teacher and the student output a probability distribution for the value of each audio sample, and the goal of the training is to minimise the KL divergence between the teacher's distribution and the student's distribution. The training method has parallels to the set-up for generative adversarial networks (GANs), with the student playing the role of generator and the teacher as the discriminator.


Google's WaveNet machine learning-based speech synthesis comes to Assistant

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WaveNet used machine learning to build a voice sample by sample, and the results were, as I put it then, "eerily convincing." The general idea behind the tech was to recreate words and sentences not by coding grammatical and tonal rules manually, but allowing a machine learning system to see those patterns in speech and generate them sample by sample. The new, improved WaveNet generates sound at 20x real time -- generating the same two-second clip in a tenth of a second. In keeping with the trend of "big tech companies doing what the other big tech companies are doing," Apple, too, recently revamped its assistant (Siri, don't you know) with a machine learning-powered speech model.


VODER (1939) - Early Speech Synthesizer

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Considered the first electrical speech synthesizer, VODER (Voice Operation DEmonstratoR) was developed by Homer Dudley at Bell Labs and demonstrated at both the 1939 New York World's Fair and the 1939 Golden Gate International Exposition. Difficult to use and difficult to operate, VODER nonetheless paved the way for future machine-generated speech.


Speech Synthesis Research Engineer ObEN, Inc.

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The work will have a particular focus on the development of structured acoustic models which take account of factors such as accent and speaking style, and on the development of machine learning techniques for vocoding. You will have the necessary programming ability to conduct research in this area, a background in statistical modeling using Hidden Markov Models, DNN, RNN, speech signal processing, and research experience in speech synthesis. A background in one or more of the following areas is also desirable: statistical parametric text-to-speech synthesis using HMMs and HSMMs; glottal source modeling; speech signal modeling; speaker adaptation using the MLLR or MAP family of techniques; familiarity with software tools including DNN, Deep Learning, RNN, HTK, HTS, Festival; and familiarity with modern machine learning. Develop and extend speech synthesis technologies in Oben's proprietary speech synthesis system, in view of the realization of prosody and voice quality modifications; Develop and apply algorithms to annotate prosody and voice quality in expressive speech synthesis corpora Carry out a listener evaluation study of expressive synthetic speech.


[P] A TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model • r/MachineLearning

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I have to warn you that I haven't had much success in generating fine samples, although the source code itself is complete. I've tried to find what's wrong, but now changed my mind to open the current code to everyone because I know many people are working on this project and my work might be a help for them.