On linear prediction models constrained to have unit-modulus poles and their use for sinusoidal frequency estimation

1988 ◽  
Vol 36 (6) ◽  
pp. 940-942 ◽  
Author(s):  
P. Stoica ◽  
A. Nehorai
Keyword(s):  
Linear Prediction ◽  
Prediction Models ◽  
Frequency Estimation

State Encoding of Hidden Markov Linear Prediction Models

1996 ◽  
Vol 29 (1) ◽  
pp. 5090-5095
Author(s):  
Vikram Krishnamurthy ◽  
H. Vincent Poor
Keyword(s):  
Linear Prediction ◽  
Prediction Models ◽  
Hidden Markov ◽  
State Encoding

Formant frequency estimation of high-pitched vowels using weighted linear prediction

2013 ◽  
Vol 134 (2) ◽  
pp. 1295-1313 ◽  
Author(s):  
Paavo Alku ◽  
Jouni Pohjalainen ◽  
Martti Vainio ◽  
Anne-Maria Laukkanen ◽  
Brad H. Story
Keyword(s):  
Linear Prediction ◽  
Frequency Estimation ◽  
Formant Frequency

Huber-Norm Regularization for Linear Prediction Models

2016 ◽  
pp. 714-730 ◽  
Author(s):  
Oleksandr Zadorozhnyi ◽  
Gunthard Benecke ◽  
Stephan Mandt ◽  
Tobias Scheffer ◽  
Marius Kloft
Keyword(s):  
Linear Prediction ◽  
Prediction Models

An Encapsulation of Vital Non-Linear Frequency Features for Various Speech Applications

2020 ◽  
Vol 17 (1) ◽  
pp. 303-307
Author(s):  
S. Lalitha ◽  
Deepa Gupta
Keyword(s):  
Speech Recognition ◽  
Linear Prediction ◽  
Performance Metrics ◽  
Speaker Identification ◽  
Frequency Estimation ◽  
Mel Frequency Cepstral Coefficients ◽  
Environment Type ◽  
Perceptual Linear Prediction ◽  
Frequency Features ◽  
Selection Of

Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual linear prediction coefficients (PLPCs) are widely casted nonlinear vocal parameters in majority of the speaker identification, speaker and speech recognition techniques as well in the field of emotion recognition. Post 1980s, significant exertions are put forth on for the progress of these features. Considerations like the usage of appropriate frequency estimation approaches, proposal of appropriate filter banks, and selection of preferred features perform a vital part for the strength of models employing these features. This article projects an overview of MFCC and PLPC features for different speech applications. The insights such as performance metrics of accuracy, background environment, type of data, and size of features are inspected and concise with the corresponding key references. Adding more to this, the advantages and shortcomings of these features have been discussed. This background work will hopefully contribute to floating a heading step in the direction of the enhancement of MFCC and PLPC with respect to novelty, raised levels of accuracy, and lesser complexity.

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