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Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum . Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.
A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. [1] The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database.
None of these voices match the Cortana text-to-speech voice which can be found on Windows Phone 8.1, Windows 10, and Windows 10 Mobile. In an attempt to unify its software with Windows 10 , all of Microsoft's current platforms use the same text-to-speech voices except for Microsoft David and a few others.
Festival Speech Synthesis System: CSTR? 2014, December MIT-like license: FreeTTS: Paul Lamere Philip Kwok Dirk Schnelle-Walka Willie Walker... 2001, December 14 2009, March 9 BSD: LumenVox: LumenVox: 2011 2019 Proprietary: Microsoft Speech API: Microsoft: 1995 2012 Bundled with Windows: VoiceText: ReadSpeaker (Formerly Neospeech) 2002 2017 ...
Speech Synthesis Markup Language (SSML) is an XML-based markup language for speech synthesis applications. It is a recommendation of the W3C's Voice Browser Working Group. SSML is often embedded in VoiceXML scripts to drive interactive telephony systems. However, it also may be used alone, such as for creating audio books.
MBROLA is speech synthesis software as a worldwide collaborative project. The MBROLA project web page provides diphone databases for many [1] spoken languages.. The MBROLA software is not a complete speech synthesis system for all those languages; the text must first be transformed into phoneme and prosodic information in MBROLA's format, and separate software (e.g. eSpeakNG) is necessary.
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