Isolated word recognition using neural network

Masood, Sarfaraz; Mehta, Madhav; Namrata, -; Rizvi, Danish Raza

doi:10.1109/indicon.2015.7443697

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…c) You can also improve the Signal-to-Noise Ratio (SNR). This filter is applied to a signal x using (1). After applying the pre-emphasis filter to the original signal, a new signal is shown, which can be seen in Fig 5. We can see that the amplitude of high frequency bands was increased and the amplitudes of lower bands was decreased so it will help to get slightly better results.…”

Section: B Feature Extractionmentioning

confidence: 99%

“…It makes the communication with computers easy and one of the ways to do that is emulating human intelligence to understand what a person says aloud. [1]. The interaction between a person and a computer using the voice becomes simpler and more comfortable because it does not need special skills such as hand coordination and speed when typing with a keyboard [2].…”

Section: Introductionmentioning

confidence: 99%

“…ASR systems of isolated words take as input individual words or a list of words with a well defined pause between them and each of the words is processed individually [1]. In this research work an ASR system is developed for isolated words, to be precise, for natural numbers from one to ten in Quechua, which is an official language in Peru.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Isolated Automatic Speech Recognition of Quechua Numbers using MFCC, DTW and KNN

Chuctaya¹,

Mercado²,

Gaona³

2018

ijacsa

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The Automatic Speech (ASR) area is defined as the transformation of acoustic signals into string words. This area has been being developed for many year facilitating the lives of people so it was implemented in several languages. However, the development of ASR in some languages with few database resources but with a large population speaking these languages is very low. The development of ASR in Quechua language is almost null which leads culture and population isolation from technology and information. In this work an ASR system of isolated Quechua numbers is developed where Mel-Frequency Cepstral Coefficients (MFCC), Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) methods are implemented using a database composed by recorded audio numbers from one to ten in Quechua. The recorded audios to feed the data base were uttered by natives man and women speakers of Quechua. The recognition accuracy reached in this research work was 91.1%.

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Section: B Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Isolated Automatic Speech Recognition of Quechua Numbers using MFCC, DTW and KNN

Chuctaya¹,

Mercado²,

Gaona³

2018

ijacsa

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show abstract

“…This study increases the accuracy of an isolated word recognition system by using the syllable number characteristics of the speech to be recognized. Most studies on speech recognition systems use utterance data in the form of isolated words (Masood et al, 2015;Hidayat et al, 2018;Raczynski, 2018;Sawant and Deshpande, 2018;Tomchuk, 2018;Winursito et al, 2018). Others developed syllable-based speech recognition systems (Can and Artuner, 2013;Soe and Theins, 2015;Kristomo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

Hidayat¹,

Winursito²

2020

IJTech

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Studies are constantly developing and improving speech recognition systems, especially their accuracy. This study developed an isolated word recognition system by using the syllable number characteristics of speech signals that will be recognized. First, the syllable number of speech signals to be recognized was detected, and then, the detection results were used to call one of the database groups that matched the syllable number characteristics. This method was designed to reduce the error possibility through a matching process between test data features and database features. This study used Mel frequency cepstral coefficients (MFCC) for feature extraction and the K-nearest neighbor (KNN) method for classification. Three versions of the proposed method were designed. The results showed that version three increased the accuracy by 4% compared to the conventional recognition system. Version three had the fastest computational time compared to the other methods. The addition of syllable detection algorithms in version three increased the computational time by only 0.151 s compared to the conventional MFCC method. The data cut length and threshold value for the filter also influenced the speech recognition system accuracy.

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A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches

Masood

Ahmad

2020

Procedia Computer Science

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Isolated word recognition using neural network

Cited by 6 publications

References 2 publications

Isolated Automatic Speech Recognition of Quechua Numbers using MFCC, DTW and KNN

Isolated Automatic Speech Recognition of Quechua Numbers using MFCC, DTW and KNN

Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches

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