On the Application of Markov Random Fields to Speech Enhancement
نویسندگان
چکیده
We report on the development of a novel Bayesian estimator for speech enhancement, which is capable of modelling the time and frequency dependencies of speech. Central to the development of the estimator is a conditional prior that is derived from the Markov Random Field theory. The proposed prior is a conditional Gaussian prior that defines the distribution of the amplitude of a speech STFT sample conditioned on the values of its time and frequency neighbours. This formulation allows the explicit inclusion in the estimation model of both time and frequency dependencies that exist among the amplitudes of speech STFT samples. The resulting estimator presents an enhanced ability in preserving the weaker speech spectral components compared to alternative estimators.
منابع مشابه
Speech 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 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 کاملHigh-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition
In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech emotion recognition. First, we extract the basic speech features including pitch frequency, formant frequency and short-term intensity. Based on the low level descriptor we further construct the statistic features including maximum, minimum,...
متن کاملSpeech Enhancement using Adaptive Data-Based Dictionary Learning
In this paper, a speech enhancement method based on sparse representation of data frames has been presented. Speech enhancement is one of the most applicable areas in different signal processing fields. The objective of a speech enhancement system is improvement of either intelligibility or quality of the speech signals. This process is carried out using the speech signal processing techniques ...
متن کاملHidden Markov Random Fields
A noninvertible function of a first order Markov process, or of a nearestneighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility. Applications include signal and image processing, speech recognition, and biological modeling. We show t...
متن کامل