Training Many Small 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...

Introducing Busy Customer Portfolio Using Hidden Markov Model

Due to the effective role of Markov models in customer relationship management (CRM), there is a lack of comprehensive literature review which contains all related literatures. In this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. One hundred articles were identified and reviewed to find direct relevance for applying Markov models...

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...

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