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  2. eSpeak - Wikipedia

    en.wikipedia.org/wiki/ESpeak

    eSpeak is a free and open-source, cross-platform, compact, software speech synthesizer.It uses a formant synthesis method, providing many languages in a relatively small file size. eSpeakNG (Next Generation) is a continuation of the original developer's project with more feedback from native speakers.

  3. Speech Recognition & Synthesis - Wikipedia

    en.wikipedia.org/wiki/Speech_Recognition_&_Synthesis

    Most voice synthesizers (including Apple's Siri) use concatenative synthesis, [5] in which a program stores individual phonemes and then pieces them together to form words and sentences. WaveNet synthesizes speech with human-like emphasis and inflection on syllables, phonemes, and words. Unlike most other text-to-speech systems, a WaveNet model ...

  4. Amazon Polly - Wikipedia

    en.wikipedia.org/wiki/Amazon_Polly

    Amazon Polly is a cloud service by Amazon Web Services, a subsidiary of Amazon.com, that converts text into spoken audio. [1] [2] [3] It allows developers to create speech-enabled applications and products. [4]

  5. Comparison of speech synthesizers - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_speech...

    Name Creator(s) First public release date Latest stable version Software license; 15.ai: 15: 2020 2022 Apple PlainTalk: Apple Inc. 1984 2018 Bundled with Mac OS X: AT&T Natural Voices

  6. MBROLA - Wikipedia

    en.wikipedia.org/wiki/MBROLA

    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.

  7. Deep learning speech synthesis - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_speech_synthesis

    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.