Evaluating the effect of voice activity detection in isolated Yoruba word recognition system

Aibinu, Abiodun Musa; Salami, Momoh Jimoh Eyiomika; Najeeb, Athaur Rahman; Azeez, Jazeem; Rajin, S. M Ataul Karim

doi:10.1109/icom.2011.5937134

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…Based on the weaknesses and strengths of the two methods, both feature extraction using MFCC and or LPC, the researchers prefer feature extraction using MFCC because the level of accuracy is better than LPC [1][7] [8]. MFCC feature extraction was between 58-75% [7]. In addition, the LPC method, research by [9] is more suitable for linear computations, whereas the human voice is essentially non-linear.…”

Section: A Feature Extraction Methodsmentioning

confidence: 99%

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Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

Heriyanto

Wahyuningrum²,

Fitriana³

2021

J.INFOTEL

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This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames with the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces a frame and cepstral coefficient as feature extraction. The use of the cepstral coefficient ranges from 0 to 23 or as many as 24 cepstral coefficients. In comparison, the captured frame consists of 0 to 10 frames or consists of eleven frames. A sound sampling of 300 recorded voice sampling was tested on 300 voice recordings of both male and female voice recordings. The frequency used is 44,100 kHz 16-bit stereo. The accuracy results show that the MFCC method with the ninth frame selection has a higher accuracy rate of 86% than other frames.

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Section: A Feature Extraction Methodsmentioning

confidence: 99%

“…According to [22], NN has a weakness in the training process, which requires a long time with a large amount of data. The same statement by [7] to identify the number one to nine utterances has a problem when the training process with massive data requires a very long processing time.…”

Section: B Matching Speech Recognitionmentioning

confidence: 99%

Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

Heriyanto

Wahyuningrum²,

Fitriana³

2021

J.INFOTEL

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“…So far, feature extraction of the most widely used speech signals is Mel-Frequency Ceptrum Coefficients (MFCC) in both speaker recognition and speech recognition by Davis and Mermelstein [8]. Feature extraction with MFCC according to Aibinu et al [9] has a level of accuracy of 58%. The recognition accuracy 75 % for MFCC by Hidayat [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Suitability of Voice Reading of Al-Qur'an Verses Based on Tajwid Using Mel Frequency Cepstral Coefficients (MFCC) and Normalization of Dominant Weight (NDW)

Heriyanto

Hartati

Putra

2018

AIVP

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The recitation of the Qur'an has its own uniqueness, among others having a special rule in reading and pronunciation, which is called tajwid science. At the time of the Qur'an is recited, there are often mistakes due to the limitations of knowledge of Tajwid. Therefore, the availability of tools to facilitate in checking the appropriateness of recitation is very much needed by those who recite the Qur'an and face limitations in understanding the science of tajwid. Checking the Qur'an reading is a problem that must be solved according to the rules. So far, voice identification studies have problems with feature extraction, compatibility or suitability testing, and accuracy. The issue of feature extraction, suitability, and impermanence testing have been improved in this study, which consists of two stages. The first stage is the extraction of the sound character of the Qur'an reading and the second stage is the testing of the conformity of the Qur'anic recitation and accuracy. In the first stage feature extraction is handled using MFCC and Normalization of Dominant Weight (NDW). Characteristics of reading the Qur'an as reference table is taken from one reader of Al-Qur'an who has competence in the field of science tajwid, for sampling 5-7 people as a source for testing. The process of the second stage of conformity testing of Qur'an reading is done starting from filtering, sequential multiplication of reference table and Conformity Uniformity Pattern (CUP). The sample of reading conformity test is taken from 11 Qur'anic letters containing 8 reading laws and 886 records. The test is performed on the dominant frame, the number of cepstral coefficient and the number of frames. The reading conformance test provides an average accuracy of 91.37% on the nine dominant frames. The test for the number of cepstral coefficients in the c-23 can be an average of 96.65%, while the number of frames on the F-10 is the best average of 96.65%.

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“…That of [20] reveals that while ANN performs better with training data and FL is better with test data with ANN models performs better than FL on the overall. Reference [26] experimented on the effect of Voice Activity Detection (VAD) on SY ASR. Hybrid of MFCC and Linear Predictive Coding (LPC) was used for feature extraction while ANN was used in recognition stage.…”

Section: Research Progress In Sy Asrmentioning

confidence: 99%

A review of Yorùbá Automatic Speech Recognition

Yusof

Atanda

Hariharan

2013

2013 IEEE 3rd International Conference on System Engineering and Technology

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Automatic Speech Recognition (ASR) has recorded appreciable progress both in technology and application. Despite this progress, there still exist wide performance gap between human speech recognition (HSR) and ASR which has inhibited its full adoption in real life situation. A brief review of research progress on Yorùbá Automatic Speech Recognition (ASR) is presented in this paper focusing of variability as factor contributing to performance gap between HSR and ASR with a view of x-raying the advances recorded, major obstacles, and chart a way forward for development of ASR for Yorùbá that is comparable to those of other tone languages and of developed nations. This is done through extensive surveys of literatures on ASR with focus on Yorùbá. Though appreciable progress has been recorded in advancement of ASR in the developed world, reverse is the case for most of the developing nations especially those of Africa. Yorùbá like most of languages in Africa lacks both human and materials resources needed for the development of functional ASR system much less taking advantage of its potentials benefits. Results reveal that attaining an ultimate goal of ASR performance comparable to human level requires deep understanding of variability factors.

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Evaluating the effect of voice activity detection in isolated Yoruba word recognition system

Cited by 7 publications

References 12 publications

Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

Evaluation of Suitability of Voice Reading of Al-Qur'an Verses Based on Tajwid Using Mel Frequency Cepstral Coefficients (MFCC) and Normalization of Dominant Weight (NDW)

A review of Yorùbá Automatic Speech Recognition

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Evaluating the effect of voice activity detection in isolated Yoruba word recognition system

Cited by 7 publications

References 12 publications

Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

Evaluation of Suitability of Voice Reading of Al-Qur'an Verses Based on Tajwid Using Mel Frequency Cepstral Coefficients (MFCC) and Normalization of Dominant Weight (NDW)

A review of Yor&#x00F9;b&#x00E1; Automatic Speech Recognition

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A review of Yorùbá Automatic Speech Recognition