Speech Recognition Using Hidden Markov Model with MFCC-Subband Technique

Patel, Ibrahim; Rao, Yanghui

doi:10.1109/itc.2010.45

Cited by 25 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MFCCs are weak for high frequency band, since Mel-scale becomes relatively inaccurate when the frequency goes up [21].…”

Section: Feature Extractionmentioning

confidence: 99%

Protected multimodal emotion recognition

Tang¹

2021

Preprint

View full text Add to dashboard Cite

In this thesis, we propose Protected Multimodal Emotion recognition (PMM-ER), an emotion recognition approach that includes security features against the growing rate of cyber-attacks on various databases, including emotion databases. The analysis on the frequently used encryption algorithms has led to the modified encryption algorithm proposed in this work. The system is able to recognize 7 different emotions, i.e. happiness, sadness, surprise, fear, disgust and anger, as well as a neutral emotion state, based on 2D video frames, 3D vertices, and audio wave information. Several well-known features are employed, including the HSV colour feature, iterative closest point (ICP) and Mel-frequency cepstral coefficients (MFCCs). We also propose a novel approach to feature fusion including both decision- and feature-level fusion, and some well-known classification and feature extraction algorithms such as principle component analysis (PCA), linear discernment analysis (LDA) and canonical correlation analysis (CCA) are compared in this study.

show abstract

“…MFCCs are weak for high frequency band, since Mel-scale becomes relatively inaccurate when the frequency goes up [21].…”

Section: Feature Extractionmentioning

confidence: 99%

Protected multimodal emotion recognition

Tang¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The time for computing the coefficients of the filtering probabilities for transition parameter variation is recorded for the Coefficient-Matrix-Fill procedure and our method (SMF) using the HMM shown in Example (1). It is shown in Figure 3, where the Coefficient-Matrix-Fill procedure is labled as CMFP.…”

Section: Run-time Analysismentioning

confidence: 99%

“…A set of stochastic processes that produces the sequence of observed symbols is used to infer an underlying stochastic process that is not observable (hidden states). HMMs have been widely utilized in many application areas including speech recognition [1], bioinformatics [2], finance [3], computer vision [4], and driver behavior modeling [5,6]. A comprehensive survey on the applications of HMMs is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models

2017

View full text Add to dashboard Cite

Abstract:In this paper, a new algorithm for sensitivity analysis of discrete hidden Markov models (HMMs) is proposed. Sensitivity analysis is a general technique for investigating the robustness of the output of a system model. Sensitivity analysis of probabilistic networks has recently been studied extensively. This has resulted in the development of mathematical relations between a parameter and an output probability of interest and also methods for establishing the effects of parameter variations on decisions. Sensitivity analysis in HMMs has usually been performed by taking small perturbations in parameter values and re-computing the output probability of interest. As recent studies show, the sensitivity analysis of an HMM can be performed using a functional relationship that describes how an output probability varies as the network's parameters of interest change. To derive this sensitivity function, existing Bayesian network algorithms have been employed for HMMs. These algorithms are computationally inefficient as the length of the observation sequence and the number of parameters increases. In this study, a simplified efficient matrix-based algorithm for computing the coefficients of the sensitivity function for all hidden states and all time steps is proposed and an example is presented.

show abstract

“…Most of the researches are focused mainly on the type of feature for the representation of speech and classifier for the separation of each class. Patel et al, introduced a speech recognition system in which mel-frequency cepstral coefficients (MFCCs) were used as feature for classification [1]. Brown et al, implemented LPC as a feature in isolated speech recognition system [2].…”

Section: Introductionmentioning

confidence: 99%

Sparse linear prediction coefficients for isolated speech recognition

Baburaj

George

2015

2015 International Conference on Control Communication &Amp; Computing India (ICCC)

View full text Add to dashboard Cite

Isolated speech recognition system is an important step in many applications such as automated banking system, catalogue dialing, automated data entry, robotics etc. Selection of feature and classifier in the speech recognition system is based on the complexity and recognition accuracy. Mel-frequency cepstral coefficients (MFCCs), line spectral frequencies (LSF), short time energy (STE) and linear prediction coefficients (LPC) are the features used in the existing speech recognition systems. In this paper, a sparse feature, obtained from the optimization of linear prediction coefficients (LPC) with a sparsity constraint is used for the classification. These sparse linear prediction coefficients (sparse LPC) offer a more effective way of representing the voiced speech. Artificial neural network (ANN) is used for the classification purpose. Experimental results show that the proposed method is noise robust and its performance exceeds LPC and MFCC feature based speech recognition systems.

show abstract

Speech Recognition Using Hidden Markov Model with MFCC-Subband Technique

Cited by 25 publications

References 5 publications

Protected multimodal emotion recognition

Protected multimodal emotion recognition

A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models

Sparse linear prediction coefficients for isolated speech recognition

Contact Info

Product

Resources

About