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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.
Gnopernicus uses these in a number of places: to know when text should and should not be interrupted, to better concatenate speech, and to sequence speech in different voices. Benchmarks conducted by Sun in 2002 on Solaris showed that FreeTTS ran two to three times faster than Flite at the time.
Glossary matches can be inserted using the mouse. The user can choose to have the source text copied into the target text field, or to have the highest fuzzy match automatically inserted. In the search window, the user can choose to search the current files' source text, target text, other translation memories, and reference files.
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.
Text-to-Speech may be used by apps such as Google Play Books for reading books aloud, Google Translate for reading aloud translations for the pronunciation of words, Google TalkBack, and other spoken feedback accessibility-based applications, as well as by third-party apps. Users must install voice data for each language.