Hidden Markov Model for Speech Recognition Using Modified Forward-Backward Re-estimation Algorithm
نویسندگان
چکیده
There are various kinds of practical implementation issues for the HMM. The use of scaling factor is the main issue in HMM implementation. The scaling factor is used for obtaining smoothened probabilities. The proposed technique called Modified Forward-Backward Re-estimation algorithm used to recognize speech patterns. The proposed algorithm has shown very good recognition accuracy as compared to the conventional Forward-Backward Re-estimation algorithm.
منابع مشابه
Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains
In this paper a framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented. Three key issues of MAP estimation, namely the choice of prior distribution family, the specification of the parameters of prior densities and the evaluation of the MAP estimates, are addressed. Using HMMs with Gaussian mixture state observation densities as an example, it is assumed ...
متن کاملSpeech enhancement based on hidden Markov model using sparse code shrinkage
This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...
متن کاملMAP Estimation of Continuous Density HMM : Theory and Applications
We discuss maximum a posteriori estimation of continuous density hidden Markov models (CDHMM). The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. Because of its adaptive nature, Bayesian learning serves as a unified approach for...
متن کاملImproving 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...
متن کاملHardware Implementation of Probabilistic State Machine for Word Recognition
Probabilistic Finite State Machines (PFSM) are used in feature Extraction, training and testing which are the most important steps in any speech recognition system. An important PFSM is the Hidden Markov Model which is dealt in this paper. This paper proposes a hardware architecture for the forward-backward algorithm as well as the Viterbi Algorithm used in speech recognition based on Hidden Ma...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012