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(); }