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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. RWTH ASR - Wikipedia

    en.wikipedia.org/wiki/RWTH_ASR

    RWTH ASR (short RASR) is a proprietary speech recognition toolkit. The toolkit includes newly developed speech recognition technology for the development of automatic speech recognition systems. It has been developed by the Human Language Technology and Pattern Recognition Group at RWTH Aachen University .

  4. 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).

  5. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    Conformer [42] and later Whisper [106] follow the same pattern for speech recognition, first turning the speech signal into a spectrogram, which is then treated like an image, i.e. broken down into a series of patches, turned into vectors and treated like tokens in a standard transformer.

  6. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules:

  7. Acoustic model - Wikipedia

    en.wikipedia.org/wiki/Acoustic_model

    An acoustic model is used in automatic speech recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech. The model is learned from a set of audio recordings and their corresponding transcripts.

  8. Timeline of speech and voice recognition - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_speech_and...

    Dragon launches Dragon Dictate, the first speech recognition product for consumers. [1] 1993: Invention: Speakable items, the first built-in speech recognition and voice enabled control software for Apple computers. 1993: Invention: Sphinx-II, the first large-vocabulary continuous speech recognition system, is invented by Xuedong Huang. [6 ...

  9. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    Transformer architecture is now used in many generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [33]

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