Warped Linear Predictive Coding of Speech Signal of Processing

2021 ◽  
Vol 9 (5) ◽  
pp. 1819-1827
Author(s):  
P. Choubey
Keyword(s):  
Speech Signal ◽  
Predictive Coding ◽  
Linear Predictive Coding

scholarly journals O USO DE REDES NEURAIS AUTO- ORGANIZÁVEIS NA VISUALIZAÇÃO DE FORMAÇÃO DE AGRUPAMENTOS A PARTIR DO CONHECIMENTO ACENTUAL DE APRENDIZES BRASILEIROS DE INGLÊS

Organon ◽  
2011 ◽  
Vol 26 (51) ◽  
Author(s):  
Ana Cristina Cunha
Keyword(s):  
Neural Network ◽  
Speech Signal ◽  
Early Stage ◽  
Predictive Coding ◽  
Lexical Stress ◽  
Self Organizing Map ◽  
L2 Acquisition ◽  
Linear Predictive Coding ◽  
Classi Cation ◽  
Basic Hypothesis

! is study aimed at investigating how Brazilian learners ofEnglish organize their knowledge about lexical stress of a speci" c wordcategory at an early stage of L2 acquisition with the help of an unsuper-vised neural network, a self-organizing map (SOM), also called Kohonennetwork. ! e basic hypothesis tested was whether the parameterization ofthe speech signal from learner’s utterances through processing techniquessuch as Linear Predictive Coding (LPC), which consisted of the input ofthe network, would be e# ective in the classi" cation of learners and theirutterances. ! e study consisted of an empirical part and a computationalone. ! e participants were beginner students aged between 18 and 25.Preliminary results indicate that the combination of LPC+SOM allowedthe creation of well-de" ned category clusters, which is an important stepin data classi" cation to aid language pro" ciency level determination, andcomputer-assisted pronunciation teaching.

scholarly journals Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

2022 ◽  
Vol 12 (2) ◽  
pp. 1982
Author(s):  
Nsiri Benayad ◽  
Zayrit Soumaya ◽  
Belhoussine Drissi Taoufiq ◽  
Ammoumou Abdelkrim
Keyword(s):  
Genetic Algorithm ◽  
Speech Signal ◽  
Wavelet Transforms ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Features Selection ◽  
Nearest Neighbour ◽  
Algorithm Optimization ◽  
Linear Predictive Coding ◽  
Zero Crossing

<span lang="EN-US">Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a <a name="_Hlk88480766"></a>linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.</span>

scholarly journals Word recognition from speech signal using linear predictive coding and spectrum analysis

2018 ◽  
Vol 7 (3) ◽  
pp. 1531
Author(s):  
Mandeep Singh ◽  
Gurpreet Singh
Keyword(s):  
Word Recognition ◽  
Sample Size ◽  
Spectrum Analysis ◽  
Speech Signal ◽  
Predictive Coding ◽  
The Other ◽  
Hybrid Technique ◽  
Linear Predictive Coding ◽  
Statistical Parameters ◽  
Isolated Word

This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing one word from the other. Second method used is based on LPC coefficients. Analysis of features extracted using LPC coefficients help in identification of a specific word from the input speech signal. Finally, a combination of best features from these two methods has been used and a hybrid technique is proposed. An accuracy of 94% has been achieved for sample size of 400 speech words.  

Exploring vowel formant estimation through simulation-based techniques

2020 ◽  
Vol 6 (s1) ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn
Keyword(s):  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Fine Grained ◽  
Vowel Formant ◽  
Simulation Based ◽  
Insight Into

AbstractThis paper contributes insight into the sources of variability in vowel formant estimation, a major analytic activity in sociophonetics, by reviewing the outcomes of two simulations that manipulated the settings used for linear predictive coding (LPC)-based vowel formant estimation. Simulation 1 explores the range of frequency differences obtained when minor adjustments are made to LPC settings, and measurement timepoints around the settings used by trained analysts, in order to determine the range of variability that should be expected in sociophonetic vowel studies. Simulation 2 examines the variability that emerges when LPC settings are varied combinatorially around constant default settings, rather than settings set by trained analysts. The impacts of different LPC settings are discussed as a way of demonstrating the inherent properties of LPC-based formant estimation. This work suggests that differences more fine-grained than about 10 Hz in F1 and 15–20 Hz in F2 are within the range of LPC-based formant estimation variability.

Power Efficient Speaker Verification Using Linear Predictive Coding on FPGA

2018 ◽  
Author(s):  
Amit Sravan Bora ◽  
Rohit Reddy ◽  
Srinath Satpathy ◽  
H. Balachander ◽  
V. Vijendra ◽  
...  
Keyword(s):  
Speaker Verification ◽  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Power Efficient

scholarly journals Navigation Security Module with Real-Time Voice Command Recognition System

2017 ◽  
Vol 24 (2) ◽  
pp. 17-26
Author(s):  
Mustafa Yagimli ◽  
Huseyin Kursat Tezer
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Predictive Coding ◽  
Recognition System ◽  
Time Warping ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Voice Command ◽  
Security Module ◽  
Dynamic Time

Abstract The real-time voice command recognition system used for this study, aims to increase the situational awareness, therefore the safety of navigation, related especially to the close manoeuvres of warships, and the courses of commercial vessels in narrow waters. The developed system, the safety of navigation that has become especially important in precision manoeuvres, has become controllable with voice command recognition-based software. The system was observed to work with 90.6% accuracy using Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) parameters and with 85.5% accuracy using Linear Predictive Coding (LPC) and DTW parameters.

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