Phoneme recognition in continuous speech using large inhomogeneous hidden Markov models

Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...

Speech Recognition Using Monophone and Triphone Based Continuous Density Hidden Markov Models

Speech Recognition is a process of transcribing speech to text. Phoneme based modeling is used where in each phoneme is represented by Continuous Density Hidden Markov Model. Mel Frequency Cepstral Coefficients (MFCC) are extracted from speech signal, delta and double-delta features representing the temporal rate of change of features are added which considerably improves the recognition accura...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Recognition of voiced sounds with a continuous state HMM

Many current speech recognition systems use very large statistical models using many thousands, perhaps millions, of parameters to account for variability in speech signals observed in large training corpora, and represent speech as sequences of discrete, independent events. The mechanisms of speech production are, however, conceptually very simple and involve continuous smooth movement of a sm...