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- #FREE MICROSOFT TTS VOICES HOW TO#
- #FREE MICROSOFT TTS VOICES FULL#
- #FREE MICROSOFT TTS VOICES CODE#
#FREE MICROSOFT TTS VOICES CODE#
Sample code for text-to-speech is available on GitHub. The text-to-speech service is available via the Speech SDK, the REST API, and the Speech CLI Sample code See the quickstart to get started with text-to-speech. Viseme is currently only supported for the en-US English (United States) neural voices. Using viseme events in Speech SDK, you can generate facial animation data, which can be used to animate faces in lip-reading communication, education, entertainment, and customer service. Visemes have a strong correlation with voices and phonemes. Visemes - Visemes are the key poses in observed speech, including the position of the lips, jaw and tongue when producing a particular phoneme.
#FREE MICROSOFT TTS VOICES HOW TO#
See how to use SSML to fine-tune the voice output for your scenario. With the multi-lingual voices, you can also adjust the speaking languages via SSML. With SSML, you can not only adjust pitch, add pauses, improve pronunciation, change speaking rate, adjust volume, and attribute multiple voices to a single document, but also define your own lexicons or switch to different speaking styles.
#FREE MICROSOFT TTS VOICES FULL#
For a full list of platform neural voices, see supported languages.įine-tune TTS output with SSML - Speech Synthesis Markup Language (SSML) is an XML-based markup language used to customize text-to-speech outputs. With the human-like natural prosody and clear articulation of words, neural voices significantly reduce listening fatigue when you interact with AI systems. Neural voices can be used to make interactions with chatbots and voice assistants more natural and engaging, convert digital texts such as e-books into audiobooks, and enhance in-car navigation systems. Prosody prediction and voice synthesis are performed simultaneously, which results in more fluid and natural-sounding outputs. Platform neural voices - Deep neural networks are used to overcome the limits of traditional speech synthesis with regard to stress and intonation in spoken language. The expectation is that requests are sent asynchronously, responses are polled for, and that the synthesized audio is downloaded when made available from the service. Unlike synthesis performed using the Speech SDK or speech-to-text REST API, responses aren't returned in real time.
Speech synthesis - Use the Speech SDK or REST API to convert text-to-speech using platform neural voices or custom neural voices.Īsynchronous synthesis of long audio - Use the Long Audio API to asynchronously synthesize text-to-speech files longer than 10 minutes (for example audio books or lectures). Migrate from Bing Speech to the Speech service.If your applications, tools, or products are using the Bing Speech APIs or Custom Speech, we've created guides to help you migrate to the Speech service. Tutorials are longer guides that show you how to use the service as a component in broader business solutions.īing Speech was decommissioned on October 15, 2019.Concepts provide in-depth explanations of the service functionality and features.How-to guides contain instructions for using the service in more specific or customized ways.Quickstarts are getting-started instructions to guide you through making requests to the service.This documentation contains the following article types: For a full list of supported voices, languages, and locales, see supported languages. Use human-like neural voices, or create a custom voice unique to your product or brand. In this overview, you learn about the benefits and capabilities of the text-to-speech service, which enables your applications, tools, or devices to convert text into human-like synthesized speech. The result is a more fluid and natural-sounding voice.
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Microsoft neural text-to-speech capability does prosody prediction and voice synthesis simultaneously, uses deep neural networks to overcome the limits of traditional text-to-speech systems in matching the patterns of stress and intonation in spoken language, and synthesizes the units of speech into a computer voice. That can result in muffled, buzzy voice synthesis.
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Traditional text-to-speech systems break down prosody into separate linguistic analysis and acoustic prediction steps that are governed by independent models. The patterns of stress and intonation in spoken language are called prosody. With the human-like natural prosody and clear articulation of words, neural text-to-speech has significantly reduced listening fatigue when you interact with AI systems. Microsoft neural text-to-speech uses deep neural networks to make the voices of computers nearly indistinguishable from recordings of people.