Vowel and consonant recognition in Turkish using neural networks toward continuous speech recognition

Parlaktuna, Osman; Cakici, Tugberk Ogulcan; Tora, Hakan; Barkana, Atalay

doi:10.1109/melcon.1994.381146

Cited by 1 publication

(3 citation statements)

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“…Table 6 depicts the sensitivity results obtained from the proposed and MFCC features classified by all three classifiers. This table also includes the classification performance of the study in [7] which classifies the Turkish vowels by MFCCs using ANN.…”

Section: Tests and Resultsmentioning

confidence: 99%

“…In order to evaluate the performance of the SV approach more objectively, a literature search on various vowel classification performances is also carried out. In detail, a brief comparison is given in Table 6 with the results of another study; however, this study also contained Turkish vowels [7] whereas we are keen to look into the success rates of vowel classification in different other languages. On the other hand, it should be pointed out that the indirect comparison here is just to give a rough idea about the performance of the SV approach among other vowel classification results in general.…”

Section: Comparison With Relevant Studiesmentioning

confidence: 99%

“…There are quite a number of studies on vowel classification in the literature. Most of them are based on frequency domain analysis using features such as formant frequencies [2,3], linear predictive coding coefficients (LPCC), perceptual linear prediction (PLP) coefficients [4], mel frequency cepstral coefficients (MFCC) [5,6,7,8], wavelets [9], spectro-temporal features [10], and spectral decomposition [11]. However, there are fewer studies using time domain analysis [12,13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vowel Classification Based on Waveform Shapes

Tora¹,

Karacor²,

Uslu³

2019

Adv. sci. technol. eng. syst. j.

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Vowel classification is an essential part of speech recognition. In classical studies, this problem is mostly handled by using spectral domain features. In this study, a novel approach is proposed for vowel classification based on the visual features of speech waveforms. In sound vocalizing, the position of certain organs of the human vocal system such as tongue, lips and jaw is very effective on the waveform shapes of the produced sound. The motivation to employ visual features instead of classical frequency domain features is its potential usage in specific applications like language education. Even though this study is confined to Turkish vowels, the developed method can be applied to other languages as well since the shapes of the vowels show similar patterns. Turkish vowels are grouped into five categories. For each vowel group, a time domain speech waveform with an interval of two pitch periods is handled as an image. A series of morphological operations is performed on this speech waveform image to obtain the geometric characteristics representing the shape of each class. The extracted visual features are then fed into three different classifiers. The classification performances of these features are compared with classical methods. It is observed that the proposed visual features achieve promising classification rates.

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Section: Tests and Resultsmentioning

confidence: 99%

Section: Comparison With Relevant Studiesmentioning

confidence: 99%