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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. Researchers say an AI-powered transcription tool used in ...

   www.aol.com/researchers-ai-powered-transcription...

   That warning hasn’t stopped hospitals or medical centers from using speech-to-text models, including Whisper, to transcribe what’s said during doctor’s visits to free up medical providers to ...

4. OpenAI open-sources Whisper, a multilingual speech ... - AOL

   www.aol.com/news/openai-open-sources-whisper...

   Speech recognition remains a challenging problem in AI and machine learning. In a step toward solving it, OpenAI today open-sourced Whisper, an automatic speech recognition system that the company ...

5. List of speech recognition software - Wikipedia

   en.wikipedia.org/wiki/List_of_speech_recognition...

   Older generations of Nokia phones like Nokia N Series (before using Windows 7 mobile technology) used speech-recognition with family names from contact list and a few commands. Siri , originally implemented in the iPhone 4S , Apple's personal assistant for iOS , which uses technology from Nuance Communications .

6. 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.

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.