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Speaker recognition is a pattern recognition problem. The various technologies used to process and store voice prints include frequency estimation , hidden Markov models , Gaussian mixture models , pattern matching algorithms, neural networks , matrix representation , vector quantization and decision trees .
It has been developed by the Human Language Technology and Pattern Recognition Group at RWTH Aachen University. RWTH ASR includes tools for the development of acoustic models and decoders as well as components for speaker adaptation, speaker adaptive training, unsupervised training, discriminative training, and word lattice processing. [1]
Speech recognition is a multi-leveled pattern recognition task. Acoustical signals are structured into a hierarchy of units, e.g. Phonemes, Words, Phrases, and Sentences; Each level provides additional constraints; e.g. Known word pronunciations or legal word sequences, which can compensate for errors or uncertainties at a lower level;
Microsoft Kinect includes built-in software which allows speech recognition of commands. 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.
Dragon NaturallySpeaking uses a minimal user interface. As an example, dictated words appear in a floating tooltip as they are spoken (though there is an option to suppress this display to increase speed), and when the speaker pauses, the program transcribes the words into the active window at the location of the cursor.
Speaker diarisation; Speakwrite; Spectral modeling synthesis; Speech analytics; Speech Application Language Tags; Speech corpus; Speech Processing Solutions; Speech recognition software for Linux; Speech repetition; SpeechCycle; SpeechWeb; SpeechWorks; Spoken dialog system; Stenomask; Subspace Gaussian mixture model; Subvocal recognition
Sound recognition is a technology, which is based on both traditional pattern recognition theories and audio signal analysis methods. Sound recognition technologies contain preliminary data processing, feature extraction and classification algorithms. Sound recognition can classify feature vectors.
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]