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The Speech Application Programming Interface or SAPI is an API developed by Microsoft to allow the use of speech recognition and speech synthesis within Windows applications. To date, a number of versions of the API have been released, which have shipped either as part of a Speech SDK or as part of the Windows OS itself.
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).
For desktop applications, other markup languages are popular, including Apple's embedded speech commands, and Microsoft's SAPI Text to speech (TTS) markup, also an XML language. It is also used to produce sounds via Azure Cognitive Services' Text to Speech API or when writing third-party skills for Google Assistant or Amazon Alexa.
Apps such as textPlus and WhatsApp use Text-to-Speech to read notifications aloud and provide voice-reply functionality. Google Cloud Text-to-Speech is powered by WaveNet, [5] software created by Google's UK-based AI subsidiary DeepMind, which was bought by Google in 2014. [6] It tries to distinguish from its competitors, Amazon and Microsoft. [7]
A speech-to-text reporter (STTR), also known as a captioner, is a person who listens to what is being said and inputs it, word for word (), as properly written texts.Many captioners use tools (such as a shorthand keyboard, speech recognition software, or a computer-aided transcription software system), which commonly convert verbally communicated information into written words to be composed ...
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.
The generated translation utterance is sent to the speech synthesis module, which estimates the pronunciation and intonation matching the string of words based on a corpus of speech data in language B. Waveforms matching the text are selected from this database and the speech synthesis connects and outputs them. [1]
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.