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  2. Whisper (speech recognition system) - Wikipedia

    en.wikipedia.org/wiki/Whisper_(speech...

    Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [2]It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [1]

  3. Transcription software - Wikipedia

    en.wikipedia.org/wiki/Transcription_software

    New software-as-a-service and cloud computing models use artificial intelligence, machine learning and natural language processing to convert speech to text and continuously learn new phrases and accents. [3] AI transcription can, however, lead to hallucinations and other errors. [4] [5] [6] [7]

  4. 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]

  5. Baidu to release next-generation AI model this year ... - AOL

    www.aol.com/news/baidu-release-next-generation...

    The new model, Ernie 5, will feature multimodal capabilities enabling it to process and convert between different formats including text, video, images and audio, CNBC reported earlier. The ...

  6. 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. [1]

  7. 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.

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