Voice recognition based on adaptive MFCC and deep learning

Bae, Hyan-Soo; Lee, Ho-Jin; Lee, Suk-Gyu

doi:10.1109/iciea.2016.7603830

Cited by 36 publications

(12 citation statements)

References 5 publications

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“…It is also known as deep structured learning. In DL, it is possible to use supervised, semisupervised, or unsupervised learning and DL is used in different fields such as natural language processing (Young et al, 2018), voice recognition (Bae et al, 2016), computer vision (Voulodimos et al, 2018), social network filtering (Nguyen et al, 2017), among others, to generate results equal to and in some cases exceeding the human expert performance (Abou Jaoude et al, 2020;Lu et al, 2020;Williams, 2020). Figure 4 Vector autoregression (VAR): Vector autoregression (VAR) is an algorithm used for multivariate forecasting of two or more time series that impact each other (Zivot et al, 2003).…”

Section: Modelsmentioning

confidence: 99%

Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

Usmani

Pillai

Hashem

et al. 2021

Preprint

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Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the Artificial Intelligence (AI) techniques. We propose the Enhanced Long Short-Term Memory (ELSTM) model and provide a comparison with other AI techniques, i.e., Long Short-Term Memory (LSTM), Deep Learning (DL), and Vector Autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

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Section: Modelsmentioning

confidence: 99%

Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

Usmani

Pillai

Hashem

et al. 2021

Preprint

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“…Pembelajaran mendalam (DL) adalah metode kecerdasan buatan (AI) yang secara matematis meniru cara kerja otak untuk menangkap pola penting dari data yang besar. Dengan meniru operasi otak, sebuah model matematika pembelajaran mendalam yang ditulis dalam program komputer dapat menyaingi, atau bahkan mengungguli manusia dalam sejumlah fungsi, seperti pengenalan gambar [11], kontrol motorik [12], dan pengenalan suara [13]. Selain itu, jaringan dalam dapat mengembangkan representasi yang lebih cocok dengan rekaman di neokorteks manusia atau primata nonmanusia daripada model yang ada dalam ilmu saraf [14].…”

Section: E Prediksi Big Data Menggunakan Deepunclassified

Peran Big Data dan Deep Learning untuk Menyelesaikan Permasalahan Secara Komprehensif

Yudistira¹

2021

Expert J. Manaj. Sist. Inf. dan Teknol.

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Peran sain data besar (Big Data) dan pembelajaran mesin dewasa ini tidak dapat terelakkan terutama untuk menganalisis data dan memberikan kecerdasan pada komputer agar bekerja secara otonom untuk menyelesaikan suatu pekerjaan tertentu. Perkembangan teknologi sensor dan internet membuat ketersediaan data tersebut melimpah yang selanjutnya dapat dilakukan analisis data dalam jumlah yang besar. Hal tersebut mempengaruhi bagaimana cara pandang komputasi dalam berbagai macam bidang baik ilmu alam maupun sosial. Data yang terkumpul dapat berupa beragam format dengan laju pertambahan yang cepat dan dinamis. Kita perlu algoritma atau model yang mumpuni untuk memahami dan menggali pengetahuan pada set data yang besar tersebut beserta rancangan modelnya yang secara otomatis mempunyai kemampuan memprediksi atau mendeteksi. Deep Learning dengan kapasitasnya yang besar serta hubungan korelasi antar neuron yang sangat banyak diharapkan mampu menjawab tantangan tersebut didukung oleh beberapa penelitian terkini pada penerapannnya di berbagai bidang keilmuan. Dalam paper ini akan dipaparkan contoh pemanfaatan Deep Learning pada Big Data yang telah kita lakukan pada pengenalan video aksi manusia pada Youtube, Segmentasi pada sel berskala besar, citra dada x-ray dan data time-series multi variabel hubungannya dengan pandemi COVID-19.

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“…Their system provided 91% accuracy in normal environmental conditions. Hyan-Soo et al [4], this research proposed an improved voice recognition approach through the use of MFCC, Adaptive MFCC, and Deep Learning. The results are a 96% recognition rate of MFCC and around 96~98% of Adaptive MFCC.…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic Speaker Identification System Using Multi Features

Mohammed¹,

Hassan²,

Ali³

2020

ETJ

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The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language. In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages. This paper aims to design an efficient SIS (text-independent) for the Arabic language. The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase. The second phase is testing, which searches the identification of the speaker. In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency. Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process. The best classification technique is a K-nearest neighbors, where it gives higher precision 94.8%.

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Voice recognition based on adaptive MFCC and deep learning

Cited by 36 publications

References 5 publications

Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

Peran Big Data dan Deep Learning untuk Menyelesaikan Permasalahan Secara Komprehensif

Arabic Speaker Identification System Using Multi Features

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