One Channel Subvocal Speech Phrases Recognition Using Cumulative Residual Entropy and Support Vector Machines

Chau, Gustavo; Kemper, Guillermo

doi:10.1109/tla.2015.7273769

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…La MSV es una técnica de clasificación, en este trabajo se usó un entrenamiento supervisado [10]. La ecuación (7) modela el proceso de clasificación de la SVM.…”

Section: Clasificación De Los Patrones De Las Señales Subvocalesunclassified

Sub auditory speech recognition based on EMG signals

Jorgensen

Lee

Agabont

Proceedings of the International Joint Conference on Neural Networks, 2003.

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ResumenPresenta una metodología novedosa para establecer una comunicación silenciosa dual basada en habla subvocal; para ello se desarrollaron dos sistemas electrónicos que registran las señales bioeléctricas que llegan al aparato fonador, generadas al momento de realizar el proceso de lectura silenciosa por el individuo. Estos sistemas están basados en tres etapas fundamentales; la primera es la de adquisición, encargada de extraer, acondicionar, codificar y transmitir las señales electromiográficas del habla subvocal hacia la segunda etapa, denominada de procesamiento; en esta etapa, implementada en un sistema Raspberry Pi, se desarrollaron los procesos de almacenamiento, acondicionamiento, extracción de patrones y clasificación de palabras, utilizando técnicas matemáticas como: Entropía, análisis Wavelet y Máquinas de Soporte Vectorial de Mínimos Cuadrados, implementadas bajo el entorno libre de programación Python; finalmente, la última etapa del sistema se encargó de comunicar inalámbricamente los dos sistemas electrónicos, utilizando 4 clases de señales, para clasificar las palabras hola, intruso, ¿hola cómo estás? y tengo frío.Adicionalmente, en este artículo se muestra la implementación del sistema para el registro de señales de habla subvocal. El porcentaje de acierto promedio general es de 72.5 %. Se incluyen un total de 50 palabras por clase, es decir, 200 señales. Finalmente, se pudo demostrar que usando una Raspberry Pi es posible establecer un sistema de comunicación silenciosa a partir de las señales del habla subvocal.Palabras clave: comunicación silenciosa; entropía; habla subvocal; MSV (Máquinas de Soporte Vectorial); Raspberry Pi; Wavelet. AbstractThis paper presents a novel methodology to develop a silent dual communication based on subvocal speech. Two electronic systems were developed for people's wireless communication. The system has 3 main stages. The first stage is the subvocal speech electromyographic signals acquisition, in charge to extract, condition, encode and transmit the system development. This signals were digitized and registered from the throat and sent to an embedded a raspberry pi.In this device was implemented the processing, as it is called the second stage, which besides to store, assumes conditioning, extraction and pattern classification of subvocal speech signals. Mathematical techniques were used as Entropy, Wavelet analysis, Minimal Squares and Vector Support Machines, which were applied in Python free environment program. Finally, in the last stage in charge to communicate by wireless means, were developed the two electronic systems, by using 4 signal types, to classify the words: Hello, intruder, hello how are you? and I am cold to perform the silent communication.Additionally, in this article we show the speech subvocal signals' recording system realization. The average accuracy percentage was 72.5 %, and includes a total of 50 words by class, this is 200 signals. Finally, it demonstrated that using the Raspberry Pi it is possible to set a silent communication ...

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“…La MSV es una técnica de clasificación, en este trabajo se usó un entrenamiento supervisado [10]. La ecuación (7) modela el proceso de clasificación de la SVM.…”

Section: Clasificación De Los Patrones De Las Señales Subvocalesunclassified

Sub auditory speech recognition based on EMG signals

Jorgensen

Lee

Agabont

Proceedings of the International Joint Conference on Neural Networks, 2003.

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“…The developed SLID model was analyzed with 10 number of population and 25 maximum numbers of iterations. The proposed meta-heuristic algorithm AD-TSA was evaluated with other algorithms such as "Particle Swarm Optimization (PSO) (16) , Crow Search Optimization (CSO) (17) , Moth flame optimization (MFO) (18) and TSA (15) ", classifiers like BP (19) , NN (20) , SVM (21) , RNN (14) . Since this the first attempt that TSA is used for language identification particulary for low resource languages that are spoken in Jammu & Kashmir along with Ladakhi language.…”

Section: Proposed Ad-tsamentioning

confidence: 99%

Improved Support Vector-Recurrent Neural Network with Optimal Feature Selection-based Spoken Language Identification System

Thukroo¹,

Bashir²,

Giri³

2023

IJST

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Objective: Spoken language identification being the fore-front of language recognition tasks and most significant medium of communication has to be enhanced in order to improve the accuracy of recently developed spoken language recognition systems. The purpose of this paper is to enhance the Spoken Language Identification (SLID) model using hybrid machine learning with deep learning model for regionally spoken languages of Jammu & Kashmir (JK) and Ladakh. Method: Initially, the speech signals of different languages of JK and Ladakh are manually collected from diverse sources, and it is preprocessed using Spectral Noise Gate (SNG) filtering technique. Once the speech signals are pre-processed, the feature extraction is performed by the cepstral features like Mel-frequency Cepstral Coefficients (MFCCs), Relative Spectral Transform-Perceptual Linear Prediction (RASTA-PLP), and spectral features like spectral roll off, spectral flatness. Findings: From this feature extraction, the length of the feature vector seems to be long, and it is required to reduce the feature length. Hence, optimal feature selection is accomplished using the new meta-heuristic algorithm termed Adaptive Distance-based Tunicate Swarm Algorithm (AD-TSA) by considering the minimum correlation as objective. Finally, the language identification is handled by the hybrid classifier termed Improved Support Vector Machine-Recurrent Neural Network (ISVM-RNN). Novelty: The identification learning algorithm is enhanced by the AD-TSA by considering the minimum correlation as objective among features in order to get minimum number of features that are sufficient for language identification process. The efficiency of the proposed hybrid approach is validated by simulating the experiment on a user-defined language database of JK and Ladakh speech signals in the working platform of Python.

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“…As far, SVM has been widely used in medical detection, text processing [18], speech recognition [19], and so on. SVM has been reported to be superior to the traditional classification method, such as BPNN [20], RBF network [21], k-Nearest Neighbors algorithm.…”

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confidence: 99%

Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

Liu¹,

Li²,

Quinn³

2018

Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

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To improve the efficiency of stomatology practitioners, this paper proposed a novel teeth type classification method. Our method was based on three successful components: Haar wavelet transform, principal component analysis, and support vector machine. We create a 120-image dataset, with 30 images for incisor, canine, premolar, and molar. The results showed our method achieved an overall classification accuracy of 81.83± 1.79%, better than decision tree and multilayer perceptron methods.

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One Channel Subvocal Speech Phrases Recognition Using Cumulative Residual Entropy and Support Vector Machines

Cited by 6 publications

References 3 publications

Sub auditory speech recognition based on EMG signals

Sub auditory speech recognition based on EMG signals

Improved Support Vector-Recurrent Neural Network with Optimal Feature Selection-based Spoken Language Identification System

Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

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