Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

Girirajan*, S.; Sangeetha, Repana Naga; Preethi, T.; Chinnappa, A.

doi:10.35940/ijrte.e6230.018520

Cited by 3 publications

(1 citation statement)

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“…Artificial neural network is used to find patterns in complex classification problems [12]. ANN is a machine learning algorithm using a dense layer as the perceptron.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Deep Learning for Infant Cry Recognition

Liang

Wijaya

Yang

et al. 2022

IJERPH

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Recognizing why an infant cries is challenging as babies cannot communicate verbally with others to express their wishes or needs. This leads to difficulties for parents in identifying the needs and the health of their infants. This study used deep learning (DL) algorithms such as the convolutional neural network (CNN) and long short-term memory (LSTM) to recognize infants’ necessities such as hunger/thirst, need for a diaper change, emotional needs (e.g., need for touch/holding), and pain caused by medical treatment (e.g., injection). The classical artificial neural network (ANN) was also used for comparison. The inputs of ANN, CNN, and LSTM were the features extracted from 1607 10 s audio recordings of infants using mel-frequency cepstral coefficients (MFCC). Results showed that CNN and LSTM both provided decent performance, around 95% in accuracy, precision, and recall, in differentiating healthy and sick infants. For recognizing infants’ specific needs, CNN reached up to 60% accuracy, outperforming LSTM and ANN in almost all measures. These results could be applied as indicators for future applications to help parents understand their infant’s condition and needs.

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“…Artificial neural network is used to find patterns in complex classification problems [12]. ANN is a machine learning algorithm using a dense layer as the perceptron.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Deep Learning for Infant Cry Recognition

Liang

Wijaya

Yang

et al. 2022

IJERPH

View full text Add to dashboard Cite

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An evaluation of deep learning approaches for detection of voice disorders.

Chinchu¹,

Kirubagari

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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The human voice manufacturing system is a complicated natural device capable of modulating pitch and loudness Human sound frequency particularly. The part in which the folded is the primary source underlying internal and/or external factors often destroys vocal folds justification. Some changes consequences are reflected in functioning emotional state soul. Therefore it essential to identify variations at an early stage gives the patient an endangerment overcome any impact modernize their quality of life will-less detection disorders using depth study methods plays an important role as has been proven to facilitate the process. Many researchers have explored technologies for streamlined that can help clinics diagnose noise paper we present the conducted research activities.

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