Speech recognition system based on short-term cepstral parameters, feature reduction method and Artificial Neural Networks

Souissi, Nawel; Cherif, Adnane

doi:10.1109/atsip.2016.7523163

Cited by 13 publications

(2 citation statements)

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“…Another voice analysis system which is based on an optimized MFCC is proposed in [36]. The system utilizes 13 original MFCCs as feature extraction and combined MFCC derivatives to discriminate between pathological and normal voices.…”

Section: Related Workmentioning

confidence: 99%

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

et al. 2021

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In the last decade, the implementation of machine learning algorithms in the analysis of voice disorder is paramount in order to provide a non-invasive voice pathology detection by only using audio signal. In spite of that, most recent systems of voice pathology work on a limited acoustic database. In other words, the systems use one vowel, such as /a/, and ignore sentences and other vowels when analyzing the audio signal. Other key issues that should be considered in the systems are accuracy and time consumption of an algorithm. Online Sequential Extreme Learning Machine (OSELM) is one of the machine learning algorithms that can be regarded as a rapid and accurate algorithm in the classification process. Therefore, this paper presents a voice pathology detection and classification system by using OSELM algorithm as a classifier, and Mel-frequency cepstral coefficient (MFCC) as a featured extraction. In this work, the voice samples were taken from the Saarbrücken voice database (SVD). This system involves two parts of the database; the first part includes all voices in SVD with sentences and vowels /a/, /i/, and /u/, which are uttered in high, low, and normal pitches; and the second part utilizes voice samples of the common three types of pathologies (cyst, polyp, and paralysis) based on the vowel /a/ that is produced in normal pitch. The experimental results have shown that OSELM was able to achieve the highest accuracy up to 91.17%, 94% of precision, and 91% of recall. Furthermore, OSELM obtained 87%, 87.55%, and 97.67% for f-measure, G-mean, and specificity, respectively. The proposed system also presents a high ability to achieve detection and classification results in real-time clinical applications.

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Section: Related Workmentioning

confidence: 99%

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

et al. 2021

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“…This process is called feature extraction and is very dependent on the particular data source and application. Much work has been done to develop sophisticated methods for such feature extraction, for example convolutional layers for image recognition [3], cepstral coefficients for speech recognition [4] or modulation recognition [5] and the bag-of words technique, developed for feature extraction of textual data [6], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

2020

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This paper presents a stochastic inference problem suited to a classification approach in a time-varying observation model with continuous-valued unknown parameterization. The utilization of an artificial neural network (ANN)-based classifier is considered, and the concept of a training process via the backpropagation algorithm is used. The main objective is the minimization of resources required for the training of the classifier in the parametric observation model. To reach this, it is proposed that the weights of the ANN classifier vary continuously with the change of the observation model parameters. This behavior is then used in an update-based backpropagation algorithm. This proposed idea is demonstrated on several procedures, which re-use previously trained weights as prior information when updating the classifier after a channel phase change. This approach successfully saves resources needed for re-training the ANN. The new approach is verified via a simulation on an example communication system with the two-way relay slowly fading channel.

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ANNs for Automatic Speech Recognition—A Survey

Jolad

Khanai

2021

Expert Clouds and Applications

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Speech recognition system based on short-term cepstral parameters, feature reduction method and Artificial Neural Networks

Cited by 13 publications

References 5 publications

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

ANNs for Automatic Speech Recognition—A Survey

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