private int typesOfErrors = 2; // shyness and eagerness, can be changed if more different types of errors are found
#endregion Fields
#region Constructors
/// <summary>
/// Constructor
/// </summary>
/// <param name="M_speakers">Number of speakers</param>
/// <param name="M_word_types">Number of word types</param>
/// <param name="M_sample">Number of samples per word type per speaker</param>
/// <param name="d">audio data</param>
/// <param name="grammar_directory">output directory of created grammars</param>
/// <param name="input">grammar file input</param>
/// <param name="number_of_alternates">number of alternative pronunciations</param>
/// <param name="r">number of repeats</param>
public Tester(int M_speakers, int M_word_types, int M_sample, Data d, string grammar_directory, GrammarReader input, int number_of_alternates, int r)
{
MWordTypes = M_word_types;
MSample = M_sample;
MSpeakers = M_speakers;
data = d;
grammarDirectory = grammar_directory;
this.input = input;
numberOfAlternates = number_of_alternates;
repeats = r;
// initialize output
SampleAccuracy = new Fraction[MSpeakers - 1, MSample, V.Length, MSpeakers, MWordTypes, MSample];
WordTypeAccuracy = new Fraction[MSpeakers - 1, MSample, V.Length, MSpeakers, MWordTypes];
OverallWordTypeAccuracy = new Fraction[MSpeakers - 1, MSample, V.Length, MWordTypes];
OverallAccuracy = new Fraction[MSpeakers-1, MSample, V.Length];
SampleDeviation = new List<int>[MSpeakers-1,MSample, MSpeakers, MWordTypes];
ConfusionMatrix = new Fraction[MSpeakers - 1, MSpeakers, MWordTypes * MSample, (numberOfAlternates * MWordTypes) + 1];
ErrorRates = new Fraction[MSpeakers - 1, MSpeakers, typesOfErrors, (numberOfAlternates * MWordTypes)];
for (int NTrainingSpeakersIndex = 0; NTrainingSpeakersIndex < MSpeakers - 1; NTrainingSpeakersIndex++)
{
for (int NTrainingSamplesPerSpeakerIndex = 0; NTrainingSamplesPerSpeakerIndex < MSample; NTrainingSamplesPerSpeakerIndex++)
{
for (int vocabSizeIndex = 0; vocabSizeIndex < V.Length; vocabSizeIndex++)
{
OverallAccuracy[NTrainingSpeakersIndex, NTrainingSamplesPerSpeakerIndex, vocabSizeIndex] = new Fraction();
for (int iTestSpeakerIndex = 0; iTestSpeakerIndex < MSpeakers; iTestSpeakerIndex++)
{
for (int iWordTypeIndex = 0; iWordTypeIndex < MWordTypes; iWordTypeIndex++)
{
WordTypeAccuracy[NTrainingSpeakersIndex, NTrainingSamplesPerSpeakerIndex, vocabSizeIndex, iTestSpeakerIndex, iWordTypeIndex] = new Fraction();
OverallWordTypeAccuracy[NTrainingSpeakersIndex, NTrainingSamplesPerSpeakerIndex, vocabSizeIndex, iWordTypeIndex] = new Fraction();
SampleDeviation[NTrainingSpeakersIndex, NTrainingSamplesPerSpeakerIndex, iTestSpeakerIndex, iWordTypeIndex] = new List<int>();
for (int iSamplesIndex = 0; iSamplesIndex < MSample; iSamplesIndex++)
{
SampleAccuracy[NTrainingSpeakersIndex, NTrainingSamplesPerSpeakerIndex, vocabSizeIndex, iTestSpeakerIndex, iWordTypeIndex, iSamplesIndex] = new Fraction();
}
}
}
}
}
}
for (int NTrainingSpeakersIndex = 0; NTrainingSpeakersIndex < MSpeakers - 1; NTrainingSpeakersIndex++)
{
for (int iTestSpeakerIndex = 0; iTestSpeakerIndex < MSpeakers; iTestSpeakerIndex++)
{
for (int i = 0; i < MSample*MWordTypes; i++)
{
for (int j = 0; j < (numberOfAlternates*MWordTypes) + 1; j++)
{
ConfusionMatrix[NTrainingSpeakersIndex, iTestSpeakerIndex, i, j] = new Fraction();
if ( i < typesOfErrors && j < numberOfAlternates*MWordTypes)
ErrorRates[NTrainingSpeakersIndex, iTestSpeakerIndex, i, j] = new Fraction();
}
}
}
}
}
public GrammarConverter(string lang, string dir, string rule_name, int M_speakers, int M_sample)
{
language = lang;
directory = dir;
ruleName = rule_name;
MSpeakers = M_speakers;
MSample = M_sample;
gr = new GrammarReader(language, directory, ruleName);
}
static void Main(string[] args)
{
// speakers to be tested
List<int> speakersToBeTested = new List<int>();
speakersToBeTested.Add(0);
speakersToBeTested.Add(1);
speakersToBeTested.Add(2);
// words to be tested
List<int> wordsToBeTested = new List<int>();
for (int i = 1; i <= 50; i += 5)
wordsToBeTested.Add(i);
//wordsToBeTested.Add(25);
// samples to be tested
List<int> samplesToBeTested = new List<int>();
samplesToBeTested.Add(1);
samplesToBeTested.Add(2);
samplesToBeTested.Add(3);
samplesToBeTested.Add(4);
samplesToBeTested.Add(5);
// audio directory
List<string> audioDirectory = new List<string>();
audioDirectory.Add("C:\\Users\\Administrator\\Documents\\Visual Studio 2008\\Projects\\cmuspeechrecognition_cmuspeechmain\\test_data\\audio\\092910_123758_Hebrew");
audioDirectory.Add("C:\\Users\\Administrator\\Documents\\Visual Studio 2008\\Projects\\cmuspeechrecognition_cmuspeechmain\\test_data\\audio\\101410_140344_Hebrew");
audioDirectory.Add("C:\\Users\\Administrator\\Documents\\Visual Studio 2008\\Projects\\cmuspeechrecognition_cmuspeechmain\\test_data\\audio\\101510_111237_Hebrew");
// audio file names
// audio file names
List<string> audioFileNames = new List<string>();
audioFileNames.Add("[Wed_(Sep_29_2010)_12-37-58]_4124143701_");
audioFileNames.Add("[Thu_(Oct_14_2010)_14-03-44]_4122688595_");
audioFileNames.Add("[Fri_(Oct_15_2010)_11-12-37]_4126203298_");
GrammarReader gr = new GrammarReader("Hebrew", "C:\\Users\\Administrator\\Documents\\Visual Studio 2008\\Projects\\cmuspeechrecognition_cmuspeechmain\\test_data", "allcombinations");
// set up data
data = new Data(audioDirectory, audioFileNames, 5, speakersToBeTested, wordsToBeTested, samplesToBeTested,
"C:\\Users\\Administrator\\Documents\\Visual Studio 2008\\Projects\\cmuspeechrecognition_cmuspeechmain\\test_data\\config_files\\config.txt.100.english");
Tester t = new Tester(MSpeakers, MWordTypes, MSamples, data, directory,null,10, 1);
t.runTestDiscriminative("multiList", new List<bool>(){true, true, true, true});
//GrammarConverter gc = new GrammarConverter("Hebrew", "C:\\Users\\cheem\\Desktop\\SalaamTest", "allcombinations", 3, 5);
//gc.convert();
}
