Data
phoneme

phoneme

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  • OpenML-CC18 OpenML100 speech recognition study_123 study_14 study_34 study_50 study_52 study_7 study_98 study_99
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Author: Dominique Van Cappel, THOMSON-SINTRA Source: [KEEL](http://sci2s.ugr.es/keel/dataset.php?cod=105#sub2), [ELENA](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/phoneme/) - 1993 Please cite: None The aim of this dataset is to distinguish between nasal (class 0) and oral sounds (class 1). Five different attributes were chosen to characterize each vowel: they are the amplitudes of the five first harmonics AHi, normalised by the total energy Ene (integrated on all the frequencies): AHi/Ene. The phonemes are transcribed as follows: sh as in she, dcl as in dark, iy as the vowel in she, aa as the vowel in dark, and ao as the first vowel in water. ### Source The current dataset was formatted by the KEEL repository, but originally hosted by the [ELENA Project](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/elena.htm#stuff). The dataset originates from the European ESPRIT 5516 project: ROARS. The aim of this project was the development and the implementation of a real time analytical system for French and Spanish speech recognition. ### Relevant information Most of the already existing speech recognition systems are global systems (typically Hidden Markov Models and Time Delay Neural Networks) which recognizes signals and do not really use the speech specificities. On the contrary, analytical systems take into account the articulatory process leading to the different phonemes of a given language, the idea being to deduce the presence of each of the phonetic features from the acoustic observation. The main difficulty of analytical systems is to obtain acoustical parameters sufficiantly reliable. These acoustical measurements must : - contain all the information relative to the concerned phonetic feature. - being speaker independent. - being context independent. - being more or less robust to noise. The primary acoustical observation is always voluminous (spectrum x N different observation moments) and classification cannot been processed directly. In ROARS, the initial database is provided by cochlear spectra, which may be seen as the output of a filters bank having a constant DeltaF/F0, where the central frequencies are distributed on a logarithmic scale (MEL type) to simulate the frequency answer of the auditory nerves. The filters outputs are taken every 2 or 8 msec (integration on 4 or 16 msec) depending on the type of phoneme observed (stationary or transitory). The aim of the present database is to distinguish between nasal and oral vowels. There are thus two different classes: - Class 0 : Nasals - Class 1 : Orals This database contains vowels coming from 1809 isolated syllables (for example: pa, ta, pan,...). Five different attributes were chosen to characterize each vowel: they are the amplitudes of the five first harmonics AHi, normalised by the total energy Ene (integrated on all the frequencies): AHi/Ene. Each harmonic is signed: positive when it corresponds to a local maximum of the spectrum and negative otherwise. Three observation moments have been kept for each vowel to obtain 5427 different instances: - the observation corresponding to the maximum total energy Ene. - the observations taken 8 msec before and 8 msec after the observation corresponding to this maximum total energy. From these 5427 initial values, 23 instances for which the amplitude of the 5 first harmonics was zero were removed, leading to the 5404 instances of the present database. The patterns are presented in a random order. ### Past Usage Alinat, P., Periodic Progress Report 4, ROARS Project ESPRIT II- Number 5516, February 1993, Thomson report TS. ASM 93/S/EGS/NC/079 Guerin-Dugue, A. and others, Deliverable R3-B4-P - Task B4: Benchmarks, Technical report, Elena-NervesII "Enhanced Learning for Evolutive Neural Architecture", ESPRIT-Basic Research Project Number 6891, June 1995 Verleysen, M. and Voz, J.L. and Thissen, P. and Legat, J.D., A statistical Neural Network for high-dimensional vector classification, ICNN'95 - IEEE International Conference on Neural Networks, November 1995, Perth, Western Australia. Voz J.L., Verleysen M., Thissen P. and Legat J.D., Suboptimal Bayesian classification by vector quantization with small clusters. ESANN95-European Symposium on Artificial Neural Networks, April 1995, M. Verleysen editor, D facto publications, Brussels, Belgium. Voz J.L., Verleysen M., Thissen P. and Legat J.D., A practical view of suboptimal Bayesian classification, IWANN95-Proceedings of the International Workshop on Artificial Neural Networks, June 1995, Mira, Cabestany, Prieto editors, Springer-Verlag Lecture Notes in Computer Sciences, Malaga, Spain

6 features

Class (target)nominal2 unique values
0 missing
V1numeric5336 unique values
0 missing
V2numeric5312 unique values
0 missing
V3numeric5308 unique values
0 missing
V4numeric5336 unique values
0 missing
V5numeric4499 unique values
0 missing

107 properties

5404
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
5
Number of numeric attributes.
1
Number of nominal attributes.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
16.67
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
0.21
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.66
Third quartile of kurtosis among attributes of the numeric type.
0.59
Average class difference between consecutive instances.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.48
Maximum skewness among attributes of the numeric type.
1
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0
Third quartile of means among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Maximum standard deviation of attributes of the numeric type.
29.35
Percentage of instances belonging to the least frequent class.
83.33
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
1586
Number of instances belonging to the least frequent class.
16.67
Percentage of nominal attributes.
1.36
Third quartile of skewness among attributes of the numeric type.
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.34
Mean kurtosis among attributes of the numeric type.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1
Third quartile of standard deviation of attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0
Mean of means among attributes of the numeric type.
0.24
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.66
First quartile of kurtosis among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
First quartile of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.34
First quartile of skewness among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.18
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.78
Mean skewness among attributes of the numeric type.
1
First quartile of standard deviation of attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.13
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
70.65
Percentage of instances belonging to the most frequent class.
1
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.87
Entropy of the target attribute values.
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
3818
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.31
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.86
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.77
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.48
Second quartile (Median) of skewness among attributes of the numeric type.

16 tasks

105424 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
102354 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
45 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1298 runs - target_feature: Class
1297 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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