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Speech-generating devices (SGDs), also known as voice output communication aids, are electronic augmentative and alternative communication (AAC) systems used to supplement or replace speech or writing for individuals with severe speech impairments, enabling them to verbally communicate. [1]
The Amazon Echo, an example of a voice computer. Voice computing is the discipline that develops hardware or software to process voice inputs. [1]It spans many other fields including human-computer interaction, conversational computing, linguistics, natural language processing, automatic speech recognition, speech synthesis, audio engineering, digital signal processing, cloud computing, data ...
Work to personalize a synthetic voice to better match a person's personality or historical voice is becoming available. [94] A noted application, of speech synthesis, was the Kurzweil Reading Machine for the Blind which incorporated text-to-phonetics software based on work from Haskins Laboratories and a black-box synthesizer built by Votrax .
1 History 2 Input methods 2.1 Fixed display devices 2.2 Dynamic display devices 2.3 Hybrid display devices 3 Output 3.1 Digitized speech 3.2 Synthesized speech 4 Selection set and vocabulary 4.1 Initial content selection 4.2 Automatic content maintenance 4.3 Ethical concerns 5 Access methods 6 Rate enhancement strategies 7 Producers 8 Notes 9 ...
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The Javanese Wikipedia (Javanese: Wikipédia basa Jawa) is the edition of Wikipedia in the Javanese language. Started on 8 March 2004, the Javanese Wikipedia reached 10,000 articles on 3 May 2007. As of 16 January 2025, it has more than 74,000 articles. [1] The Indonesian media has discussed the Javanese Wikipedia. [2]
In the 1990s, improvements in voice recognition technology began to allow computers to transcribe recorded audio dictation into text form, a task that previously required human secretaries or transcribers. The files generated with digital recorders vary in size, depending on the manufacturer and the format the user chooses.
A stack of dilated casual convolutional layers used in WaveNet [1]. In September 2016, DeepMind proposed WaveNet, a deep generative model of raw audio waveforms, demonstrating that deep learning-based models are capable of modeling raw waveforms and generating speech from acoustic features like spectrograms or mel-spectrograms.