Speech Recognition

アルゴリズム:Algorithms

Protected: Model Building and Inference in Bayesian Inference – Overview and Models of Hidden Markov Models

Model building and inference of Bayesian inference for digital transformation, artificial intelligence, and machine learning tasks - Overview of hidden Markov models and models eigenvalues, hyperparameters, conjugate prior, gamma prior, sequence analysis, gamma distribution, Poisson distribution, mixture models graphical model, simultaneous distribution, transition probability matrix, latent variable, categorical distribution, Dirichlet distribution, state transition diagram, Markov chain, initial probability, state series, sensor data, network logs, speech recognition, natural language processing
機械学習:Machine Learning

Protected: Large-vocabulary continuous speech recognition

Overview of large-vocabulary continuous speech recognition, a technology for recognizing unconstrained general speech in speech recognition technology used for digital transformation and artificial intelligence tasks.
機械学習:Machine Learning

Protected: Language Models in Speech Recognition

Language models (n-gram models and probabilistic models) used in speech recognition for digital transformation and artificial intelligence tasks.
機械学習:Machine Learning

Protected: Application of Hidden Markov Models to Speech Recognition

Overview of hidden Markov models (HMMs) for speech recognition for use in digital transformation and artificial intelligence tasks (Baum-Welch algorithm, Viterbi algorithm, EM algorithm, CDHMM).
機械学習:Machine Learning

Protected: Speech Analysis:AD transform, analytical window, Fourier transform and vector quantization

Pre-processing of speech recognition for digital transformation and artificial intelligence tasks, from AD conversion to feature extraction and vector quantization
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