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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.
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;
Other typical applications of pattern recognition techniques are automatic speech recognition, speaker identification, classification of text into several categories (e.g., spam or non-spam email messages), the automatic recognition of handwriting on postal envelopes, automatic recognition of images of human faces, or handwriting image ...
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.
Early attempts at speech processing and recognition were primarily focused on understanding a handful of simple phonetic elements such as vowels. In 1952, three researchers at Bell Labs, Stephen. Balashek, R. Biddulph, and K. H. Davis, developed a system that could recognize digits spoken by a single speaker. [2]
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
Sphinx is a continuous-speech, speaker-independent recognition system making use of hidden Markov acoustic models and an n-gram statistical language model. It was developed by Kai-Fu Lee. Sphinx featured feasibility of continuous-speech, speaker-independent large-vocabulary recognition, the possibility of which was in dispute at the time (1986).
For speech recognition on a standard desktop PC, the limiting factor is the sound card. Most sound cards today can record at sampling rates of between 16 kHz-48 kHz of audio, with bit rates of 8 to 16-bits per sample, and playback at up to 96 kHz.