The service applies the rules to convert the ordinary (orthographic) spelling of each word to a phonetic spelling. In order to address these challenges, our … team has developed a new method for neural speech synthesis based on a modular architecture. When you synthesize text with IBM Watson® Text to Speech, the service applies language-dependent pronunciation rules. “Yet, to produce this high-quality speech, most TTS systems depend on large and complex neural network models that are difficult to train and do not allow real-time speech synthesis, even when leveraging GPUs. “Recent advances in deep learning are dramatically improving the development of Text-to-Speech (TTS) systems through more effective and efficient learning of voice and speaking styles of speakers and more natural generation of high-quality output speech,” wrote IBM researchers Zvi Kons, Slava Shechtman, and Alex Sorin in a blog post accompanying a preprint paper presented at Interspeech 2019. About two years ago, we introduced our latest generation of Text-to-Speech (TTS) Neural Voices, bringing state-of-the-art natural-sounding voices into voice solutions. They say it’s able to synthesize high-quality speech in real time by learning different aspects of a speaker’s voice, making it possible to adapt to new speaking styles and voices with small amounts of data. In pursuit of less demanding models, researchers at IBM developed a new lightweight and modular method for speech synthesis. A recent study published by OpenAI drives the point home - it found that since 2012, the amount of compute used in the largest runs grew by more than 300,000 times. Training powerful text to speech models requires sufficiently powerful hardware. IBM Watson Speech to Text software helps businesses draw insights from their data by using machine learning technology and AI algorithms to transform spoken. Convert written text to natural-sounding speech using IBM Watsons latest neural speech synthesizing techniques.

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