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  2. Audio deepfake - Wikipedia

    en.wikipedia.org/wiki/Audio_deepfake

    It is necessary to collect clean and well-structured raw audio with the transcripted text of the original speech audio sentence. Second, the text-to-speech model must be trained using these data to build a synthetic audio generation model. Specifically, the transcribed text with the target speaker's voice is the input of the generation model.

  3. Speech recognition - Wikipedia

    en.wikipedia.org/wiki/Speech_recognition

    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).

  4. Retrieval-based Voice Conversion - Wikipedia

    en.wikipedia.org/wiki/Retrieval-Based_Voice...

    Retrieval-based Voice Conversion (RVC) is an open source voice conversion AI algorithm that enables realistic speech-to-speech transformations, accurately preserving the intonation and audio characteristics of the original speaker.

  5. Otter.ai - Wikipedia

    en.wikipedia.org/wiki/Otter.ai

    Otter.ai, Inc. is an American transcription software company based in Mountain View, California. The company develops speech to text transcription applications using artificial intelligence and machine learning. Its software, called Otter, shows captions for live speakers, and generates written transcriptions of speech. [1]

  6. Compression artifact - Wikipedia

    en.wikipedia.org/wiki/Compression_artifact

    Original image, with good text edges and color grade Loss of edge clarity and tone "fuzziness" in heavy JPEG compression. A compression artifact (or artefact) is a noticeable distortion of media (including images, audio, and video) caused by the application of lossy compression.

  7. Wikipedia:WikiProject Spoken Wikipedia - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:WikiProject...

    The Audio Barnstar is more general and may be awarded to editors who make a significant contribution to the wiki by creating and/or adding original or rare audio files, historical recordings, self-made music, self-made examples of sound effects or musical styles, natural sounds, etc.

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