예제 #1
0
파일: MScore.java 프로젝트: crs4/ACTIVE
  public void printTheBestByThr(long thr) {
    System.out.println("------THE BEST BY SPEAKERil------");
    Hashtable cluster = clusterResultSet.getCluster();
    Iterator<String> it = cluster.keySet().iterator();
    Hashtable<String, Vector> speaker = new Hashtable<String, Vector>();
    while (it.hasNext()) {
      String cr_it = (String) it.next();
      // System.out.println(cr_it);
      ClusterResult cr = (ClusterResult) cluster.get(cr_it);
      Object[] db_arr = cr.getValue().keySet().toArray();
      Arrays.sort(db_arr);
      int ln = db_arr.length;
      if (speaker.keySet().contains((String) cr.getValue().get(db_arr[ln - 1]))) {
        Vector<String> tmp = speaker.get(cr.getValue().get(db_arr[ln - 1]));
        tmp.add(cr_it);
        speaker.put((String) cr.getValue().get(db_arr[ln - 1]), tmp);
      } else {
        Vector<String> tmp = new Vector<String>();
        tmp.add(cr_it);
        speaker.put((String) cr.getValue().get(db_arr[ln - 1]), tmp);
      }
      // System.out.println("score="+db_arr[ln-1] +" name="+cr.getValue().get(db_arr[ln-1])  );
    }
    Iterator<String> sp_it = speaker.keySet().iterator();
    // String f
    // ="/Users/labcontenuti/Documents/workspace/AudioActive/84/test_file/properties/testindent.txt";

    OutputStreamWriter dos;
    try {
      dos =
          new OutputStreamWriter(new FileOutputStream(outputRoot + "/" + baseName + "_ident.txt"));

      while (sp_it.hasNext()) {
        String key = (String) sp_it.next();
        System.out.println("name=" + key);
        for (int i = 0; i < ((Vector) speaker.get(key)).size(); i++) {
          TreeMap<Integer, Segment> map =
              clusterSetResult
                  .getCluster((String) ((Vector) speaker.get(key)).get(i))
                  .clusterToFrames();
          System.out.println(
              " cluster="
                  + ((Vector) speaker.get(key)).get(i)
                  + " lenght="
                  + clusterSetResult
                      .getCluster((String) ((Vector) speaker.get(key)).get(i))
                      .getLength());
          dos.write(((Vector) speaker.get(key)).get(i) + "=" + key + "\n");
        }
      }

      dos.close();
    } catch (IOException e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
  }
예제 #2
0
  /**
   * Decode.
   *
   * @param nbComp the nb comp
   * @param threshold the threshold
   * @param clusterSet the cluster set
   * @param featureSet the feature set
   * @param parameter the parameter
   * @return the cluster set
   * @throws Exception the exception
   */
  public ClusterSet decode(
      int nbComp,
      double threshold,
      ClusterSet clusterSet,
      AudioFeatureSet featureSet,
      Parameter parameter)
      throws Exception {
    String mask = parameter.getParameterSegmentationOutputFile().getMask();
    String oldModelKind = parameter.getParameterModel().getModelKindAsString();
    int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents();

    // ** Train GMM for each cluster.
    // ** GMM is a 8 component gaussian with diagonal covariance matrix
    // ** one GMM = one speaker = one cluster
    // ** initialization of the GMMs :
    // ** - same global covariance for each gaussian,
    // ** - 1/8 for the weight,
    // ** - means are initialized with the mean of 10 successive vectors taken
    parameter.getParameterModel().setModelKind("DIAG");
    parameter.getParameterModel().setNumberOfComponents(nbComp);
    GMMArrayList gmmInitVect = new GMMArrayList(clusterSet.clusterGetSize());
    MTrainInit.make(featureSet, clusterSet, gmmInitVect, parameter);
    // ** EM training of the initialized GMM
    GMMArrayList gmmVect = new GMMArrayList(clusterSet.clusterGetSize());
    MTrainEM.make(featureSet, clusterSet, gmmInitVect, gmmVect, parameter);

    // ** set the penalty to move from the state i to the state j, penalty to move from i to i is
    // equal to 0
    parameter.getParameterDecoder().setDecoderPenalty(String.valueOf(threshold));
    // ** make Viterbi decoding using the 8-GMM set
    // ** one state = one GMM = one speaker = one cluster
    ClusterSet clustersDClust = MDecode.make(featureSet, clusterSet, gmmVect, parameter);
    if (parameter.getParameterDiarization().isSaveAllStep()) {
      parameter.getParameterSegmentationOutputFile().setMask(mask + ".d.seg");
      MainTools.writeClusterSet(parameter, clustersDClust, false);
    }
    // ** move the boundaries of the segment in low energy part of the signal
    ClusterSet clustersAdjClust = SAdjSeg.make(featureSet, clustersDClust, parameter);
    if (parameter.getParameterDiarization().isSaveAllStep()) {
      parameter.getParameterSegmentationOutputFile().setMask(mask + ".adj.seg");
      MainTools.writeClusterSet(parameter, clustersAdjClust, false);
    }

    parameter.getParameterSegmentationOutputFile().setMask(mask);
    parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent);
    parameter.getParameterModel().setModelKind(oldModelKind);
    return clustersAdjClust;
  }
예제 #3
0
  /**
   * Sanity check.
   *
   * @param clusterSet the cluster set
   * @param featureSet the feature set
   * @param parameter the parameter
   * @return the cluster set
   * @throws DiarizationException the diarization exception
   * @throws IOException Signals that an I/O exception has occurred.
   * @throws ParserConfigurationException the parser configuration exception
   * @throws SAXException the sAX exception
   * @throws TransformerException the transformer exception
   */
  public ClusterSet sanityCheck(
      ClusterSet clusterSet, AudioFeatureSet featureSet, Parameter parameter)
      throws DiarizationException, IOException, ParserConfigurationException, SAXException,
          TransformerException {
    String mask = parameter.getParameterSegmentationOutputFile().getMask();

    ClusterSet clustersSegInit = new ClusterSet();
    MSegInit.make(featureSet, clusterSet, clustersSegInit, parameter);
    clustersSegInit.collapse();
    if (parameter.getParameterDiarization().isSaveAllStep()) {
      parameter.getParameterSegmentationOutputFile().setMask(mask + ".i.seg");
      MainTools.writeClusterSet(parameter, clustersSegInit, false);
    }

    parameter.getParameterSegmentationOutputFile().setMask(mask);

    return clustersSegInit;
  }
예제 #4
0
 /** Run. */
 public void run() {
   ClusterSet clusterSet = getNextClusterSet();
   while (clusterSet != null) {
     Parameter parameter = getParameter(arguments);
     parameter.show = clusterSet.getShowNames().first();
     logger.finer("-------------------------------------------");
     logger.finer("--- " + parameter.show + " ---");
     logger.finer("-------------------------------------------");
     TreeMap<String, DiarizationResultList> showResult;
     try {
       showResult = tunEster2Diarization(parameter, clusterSet);
       sumResult(showResult);
       System.gc();
     } catch (DiarizationException e) {
       logger.log(Level.SEVERE, "Diarization error", e);
       e.printStackTrace();
     } catch (Exception e) {
       logger.log(Level.SEVERE, "Exception error", e);
       e.printStackTrace();
     }
     clusterSet = getNextClusterSet();
   }
 }
예제 #5
0
 /**
  * Initialize.
  *
  * @param parameter the parameter
  * @return the cluster set
  * @throws DiarizationException the diarization exception
  * @throws Exception the exception
  */
 public ClusterSet initialize(Parameter parameter) throws DiarizationException, Exception {
   // ** get the first diarization
   logger.info("Initialize segmentation");
   ClusterSet clusterSet = null;
   if (parameter.getParameterDiarization().isLoadInputSegmentation()) {
     clusterSet = MainTools.readClusterSet(parameter);
     // seg IRIT
     // return clusterSet;
     // seg IRIT
   } else {
     clusterSet = new ClusterSet();
     Cluster cluster = clusterSet.createANewCluster("init");
     Segment segment =
         new Segment(
             parameter.show,
             0,
             1,
             cluster,
             parameter.getParameterSegmentationInputFile().getRate());
     cluster.addSegment(segment);
   }
   return clusterSet;
 }
예제 #6
0
  /**
   * Tun ester2 speaker clr clustering.
   *
   * @param referenceClusterSet the reference cluster set
   * @param uemClusterSet the uem cluster set
   * @param partialKey the partial key
   * @param method the method
   * @param clusterSetBase the cluster set base
   * @param clusterSet the cluster set
   * @param featureSet the feature set
   * @param parameter the parameter
   * @return the diarization result list
   * @throws Exception the exception
   */
  public DiarizationResultList tunEster2SpeakerCLRClustering(
      ClusterSet referenceClusterSet,
      ClusterSet uemClusterSet,
      String partialKey,
      String method,
      ClusterSet clusterSetBase,
      ClusterSet clusterSet,
      AudioFeatureSet featureSet,
      Parameter parameter)
      throws Exception {
    String oldSpeechDetectorMethod = parameter.getParameterInputFeature().getSpeechMethodAsString();
    double oldSpeechDetectorThreshold = parameter.getParameterInputFeature().getSpeechThreshold();
    String oldModelKind = parameter.getParameterModel().getModelKindAsString();
    int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents();
    String oldMethod = parameter.getParameterClustering().getMethodAsString();
    double oldThreshold = parameter.getParameterClustering().getThreshold();

    String oldEMControl = parameter.getParameterEM().getEMControl();
    int oldNTop = parameter.getParameterTopGaussian().getScoreNTop();
    boolean oldSaveAll = parameter.getParameterDiarization().isSaveAllStep();

    DiarizationResultList localResult = new DiarizationResultList(cMin, cMax, mult);
    DiarizationError computeError = new DiarizationError(referenceClusterSet, uemClusterSet);
    double prevScore = cMin;

    // ---- Begin NEW v 1.13 ---
    parameter.getParameterInputFeature().setSpeechMethod("E");
    parameter.getParameterInputFeature().setSpeechThreshold(0.1);

    // ---- End NEW v 1.13 ---
    String FeatureFormat = "featureSetTransformation";
    String dir = "ester2";
    InputStream ubmInputStream = getClass().getResourceAsStream(dir + "/ubm.gmm");
    GMMArrayList ubmVect =
        MainTools.readGMMContainer(ubmInputStream, parameter.getParameterModel());
    GMM ubm = ubmVect.get(0);
    /*
     * int nbCep = 16; logger.info("---> nbCep:"+nbCep); FeatureSet featureSet2 = loadFeature(parameter, clusterSet, "audio16kHz2sphinx,1:3:2:0:0:0,"+nbCep+",1:1:300:4"); logger.info("---> nbCep:"+nbCep); //logger.fine("*** nbFeaturesNorm:" +
     * clusterSetBase.getLength());
     */
    // A tester

    AudioFeatureSet featureSet2 =
        loadFeature(featureSet, parameter, clusterSet, FeatureFormat + ",1:3:2:0:0:0,13,1:1:300:4");
    // v5.14
    // FeatureSet featureSet2 = loadFeature(featureSet, parameter, clusterSet, FeatureFormat
    // + ",1:3:2:0:0:0,13,1:1:0:0");
    parameter.getParameterModel().setModelKind("DIAG");
    parameter.getParameterModel().setNumberOfComponents(ubm.getNbOfComponents());
    parameter.getParameterClustering().setMethod(method);
    // ---- Begin NEW v 1.19 ---
    // parameter.getParameterEM().setEMControl("1,1,0.01");
    // parameter.getParameterClustering().setThreshold(0);
    // ---- End NEW v 1.19 ---
    parameter.getParameterClustering().setThreshold(cMax);
    parameter.getParameterEM().setEMControl("1,5,0.01");
    parameter.getParameterTopGaussian().setScoreNTop(5);
    parameter.getParameterDiarization().setSaveAllStep(false);

    CLRHClustering clustering = new CLRHClustering(clusterSet, featureSet2, parameter, ubm);
    // int nbCluster = clusterSet.clusterGetSize();
    // logger.info("initialise clustering CLR clusterSet:"+clusterSet);
    clustering.initialize();

    double score = clustering.getScoreOfCandidatesForMerging();
    DiarizationResult error = computeError.scoreOfMatchedSpeakers(clustering.getClusterSet());
    double errorRate = error.getErrorRate();
    localResult.setResult(prevScore, score, error);
    // prevScore = Math.max(score, prevScore);
    logger.fine(
        "first "
            + parameter.show
            + " key="
            + partialKey
            + " clrScore="
            + score
            + " clrErrorRate="
            + errorRate
            + " clrSize="
            + clustering.getSize()
            + "/"
            + referenceClusterSet.clusterGetSize());
    while ((score < cMax) && (clustering.getSize() > 1)) {
      localResult.setResult(prevScore, score, error);
      prevScore = Math.max(score, prevScore);
      clustering.mergeCandidates();

      // -- start V5.16 --
      // logger.info("--> Decoding");
      // ClusterSet decodeClusterSet = MDecode.make(featureSet2, clustering.getClusterSet(),
      // clustering.getGmmList(), parameter);
      // logger.info("--> Clustering");
      // featureSet2 = loadFeature(featureSet, parameter, decodeClusterSet, FeatureFormat
      // + ",1:3:2:0:0:0,13,1:1:300:4");
      // clustering = new CLRHClustering(decodeClusterSet, featureSet2, parameter, ubm);
      // clustering.initialize();
      // -- end V5.16 --

      score = clustering.getScoreOfCandidatesForMerging();
      error = computeError.scoreOfMatchedSpeakers(clustering.getClusterSet());
      errorRate = error.getErrorRate();
      // localResult.setResult(prevScore, score, error);
      // prevScore = Math.max(score, prevScore);
      logger.fine(
          parameter.show
              + " key="
              + partialKey
              + " clrScore="
              + score
              + " clrErrorRate="
              + errorRate
              + " clrSize="
              + clustering.getSize()
              + "/"
              + referenceClusterSet.clusterGetSize());
    }
    localResult.setResult(prevScore, score, error);
    localResult.setResult(score, cMax, error);
    logger.finer(parameter.show + " key=" + partialKey + " resultat du fichier");
    localResult.log("partial result: " + parameter.show + " " + partialKey);

    clustering.reset();

    parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent);
    parameter.getParameterModel().setModelKind(oldModelKind);
    parameter.getParameterClustering().setMethod(oldMethod);
    parameter.getParameterClustering().setThreshold(oldThreshold);
    parameter.getParameterEM().setEMControl(oldEMControl);
    parameter.getParameterTopGaussian().setScoreNTop(oldNTop);
    parameter.getParameterInputFeature().setSpeechMethod(oldSpeechDetectorMethod);
    parameter.getParameterInputFeature().setSpeechThreshold(oldSpeechDetectorThreshold);
    parameter.getParameterDiarization().setSaveAllStep(oldSaveAll);

    return localResult;
  }
예제 #7
0
  /**
   * Tun ester2 diarization.
   *
   * @param parameter the parameter
   * @param clusterSet the cluster set
   * @return the tree map
   * @throws DiarizationException the diarization exception
   * @throws Exception the exception
   */
  public TreeMap<String, DiarizationResultList> tunEster2Diarization(
      Parameter parameter, ClusterSet clusterSet) throws DiarizationException, Exception {

    TreeMap<String, DiarizationResultList> result = new TreeMap<String, DiarizationResultList>();

    // double paramThr = parameter.getParameterClustering().getThreshold();
    lMin = parameter.getParameterDiarization().getThreshold("l");
    lMax = parameter.getParameterDiarization().getMaxThreshold("l");
    hMin = parameter.getParameterDiarization().getThreshold("h");
    hMax = parameter.getParameterDiarization().getMaxThreshold("h");
    dMin = parameter.getParameterDiarization().getThreshold("d");
    dMax = parameter.getParameterDiarization().getMaxThreshold("d");
    cMin = parameter.getParameterDiarization().getThreshold("c");
    cMax = parameter.getParameterDiarization().getMaxThreshold("c");

    String featureDesc = parameter.getParameterInputFeature().getFeaturesDescriptorAsString();

    AudioFeatureSet featureSet = null;
    ClusterSet clustersSegInit = null;

    if (parameter.getParameterDiarization().isLoadInputSegmentation() == false) {
      featureSet = loadFeature(parameter, clusterSet, featureDesc);
      featureSet.setCurrentShow(parameter.show);
      int nbFeatures = featureSet.getNumberOfFeatures();
      clusterSet.getFirstCluster().firstSegment().setLength(nbFeatures);
      clustersSegInit = sanityCheck(clusterSet, featureSet, parameter);
    } else {
      featureSet = loadFeature(parameter, clusterSet, featureDesc);
      featureSet.setCurrentShow(parameter.show);
      clustersSegInit = sanityCheck(clusterSet, featureSet, parameter);
      featureSet = loadFeature(parameter, clustersSegInit, featureDesc);
      featureSet.setCurrentShow(parameter.show);
    }

    // seg IRIT
    // ClusterSet clustersSegSave = clustersSegInit;
    // seg IRIT
    ClusterSet referenceClusterSet = MainTools.readTheSecondClusterSet(parameter);
    ClusterSet uemClusterSet = MainTools.readThe3rdClusterSet(parameter);

    if (parameter.getParameterDiarization().isLastStepOnly()) {
      String key = "l=" + lMin + " h=" + hMin + " d=" + dMin;
      DiarizationResultList values = null;
      if (parameter.getParameterDiarization().isCEClustering() == false) {
        logger.warning(" nothing to do isCEClustering == false");
      } else {
        values =
            tunEster2SpeakerCLRClustering(
                referenceClusterSet,
                uemClusterSet,
                key,
                "ce",
                clusterSet,
                clusterSet,
                featureSet,
                parameter);
      }
      result.put(key, values);
      return result;
    }

    ClusterSet clustersSegSave =
        segmentation("GLR", "FULL", clustersSegInit, featureSet, parameter);
    for (double l = lMin; l <= lMax; l += 0.5) {
      ClusterSet clustersSeg = clustersSegSave.clone();
      logger.finest("clustering l=" + l);
      ClusterSet clustersLClust = clusteringLinear(l, clustersSeg, featureSet, parameter);
      // ---- Begin NEW v 1.14 ---
      for (double h = hMin; h <= hMax; h += 0.5) {
        // for (double h = hMin; h <= hMax; h += 0.2) {
        // ---- end NEW v 1.14 ---
        // if (h > l) {
        ClusterSet clustersHClust = clustering(h, clustersLClust, featureSet, parameter);
        for (double d = dMin; d <= dMax; d += 50) {
          ClusterSet clustersDClust = decode(8, d, clustersHClust, featureSet, parameter);
          // double error = DiarizationError.scoreOfMatchedSpeakers(referenceClusterSet,
          // clustersDClust);
          ClusterSet clustersSplitClust =
              speech(
                  "10,10,50", clusterSet, clustersSegInit, clustersDClust, featureSet, parameter);
          ClusterSet clustersGender = gender(clusterSet, clustersSplitClust, featureSet, parameter);

          String key = "l=" + l + " h=" + h + " d=" + d;
          DiarizationResultList values = null;
          if (parameter.getParameterDiarization().isCEClustering() == false) {
            values = new DiarizationResultList(0, 0, 1);
            DiarizationError computeError =
                new DiarizationError(referenceClusterSet, uemClusterSet);
            DiarizationResult error = computeError.scoreOfMatchedSpeakers(clustersGender);
            values.setResult(0, 0, error);
            logger.finer(parameter.show + " key=" + key + " resultat du fichier");
            values.log("partial result: " + parameter.show + " " + key);
          } else {
            // V4.19 = CLUST_H_BIC_GMM_MAP
            // values = tunEster2SpeakerCLRClustering(referenceClusterSet, key, "bicgmmmap",
            // clustersGender, clustersGender, featureSet, parameter);
            // V5.16 = ce_d
            // values = tunEster2SpeakerCLRClustering(referenceClusterSet, key, "ce_d",
            // clustersGender, clustersGender, featureSet, parameter);
            values =
                tunEster2SpeakerCLRClustering(
                    referenceClusterSet,
                    uemClusterSet,
                    key,
                    "ce",
                    clustersGender,
                    clustersGender,
                    featureSet,
                    parameter);
          }
          if (result.containsKey(key)) {
            result.get(key).addResultArray(values);
          } else {
            result.put(key, values);
          }
        }
        // }
      }
    }
    return result;
  }
예제 #8
0
  /**
   * Ester2 version.
   *
   * @param parameter the parameter
   * @throws DiarizationException the diarization exception
   * @throws Exception the exception
   */
  public void ester2Version(Parameter parameter) throws DiarizationException, Exception {

    // ** Caution this system is developed using Sphinx MFCC computed with legacy mode
    ClusterSet referenceClusterSet = null;
    if (!parameter.getParameterSegmentationInputFile2().getMask().equals("")) {
      referenceClusterSet = MainTools.readTheSecondClusterSet(parameter);
    }
    ClusterSet uemClusterSet = null;
    if (!parameter.getParameterSegmentationInputFile3().getMask().equals("")) {
      referenceClusterSet = MainTools.readThe3rdClusterSet(parameter);
    }
    ParameterBNDiarization parameterDiarization = parameter.getParameterDiarization();
    // ** mask for the output of the segmentation file

    ClusterSet clusterSet = initialize(parameter);

    // ** load the features, sphinx format (13 MFCC with C0) or compute it form a wave file
    AudioFeatureSet featureSet =
        loadFeature(
            parameter,
            clusterSet,
            parameter.getParameterInputFeature().getFeaturesDescriptorAsString());
    featureSet.setCurrentShow(parameter.show);
    int nbFeatures = featureSet.getNumberOfFeatures();
    if (parameter.getParameterDiarization().isLoadInputSegmentation() == false) {
      clusterSet.getFirstCluster().firstSegment().setLength(nbFeatures);
    }
    // clusterSet.debug(3);
    ClusterSet clustersSegInit = sanityCheck(clusterSet, featureSet, parameter);
    ClusterSet clustersSeg = segmentation("GLR", "FULL", clustersSegInit, featureSet, parameter);
    // Seg IRIT
    // ClusterSet clustersSegInit = clusterSet;
    // ClusterSet clustersSeg = clusterSet;
    // Seg IRIT
    ClusterSet clustersLClust =
        clusteringLinear(
            parameterDiarization.getThreshold("l"), clustersSeg, featureSet, parameter);
    ClusterSet clustersHClust =
        clustering(parameterDiarization.getThreshold("h"), clustersLClust, featureSet, parameter);
    // MainTools.writeClusterSet(parameter, clustersHClust, false);

    ClusterSet clustersDClust =
        decode(8, parameterDiarization.getThreshold("d"), clustersHClust, featureSet, parameter);
    ClusterSet clustersSplitClust =
        speech("10,10,50", clusterSet, clustersSegInit, clustersDClust, featureSet, parameter);
    ClusterSet clustersGender = gender(clusterSet, clustersSplitClust, featureSet, parameter);

    if (parameter.getParameterDiarization().isCEClustering()) {
      ClusterSet clustersCLR =
          speakerClustering(
              parameterDiarization.getThreshold("c"),
              "ce",
              clustersSegInit,
              clustersGender,
              featureSet,
              parameter);
      MainTools.writeClusterSet(parameter, clustersCLR, false);
      if (referenceClusterSet != null) {
        DiarizationError computeError = new DiarizationError(referenceClusterSet, uemClusterSet);
        computeError.scoreOfMatchedSpeakers(clustersCLR);
      }
    } else {
      MainTools.writeClusterSet(parameter, clustersGender, false);
    }
  }
예제 #9
0
파일: MScore.java 프로젝트: crs4/ACTIVE
  public ClusterSet make(
      AudioFeatureSet featureSet,
      ClusterSet clusterSet,
      GMMArrayList gmmList,
      GMMArrayList gmmTopList,
      Parameter parameter)
      throws DiarizationException, IOException {
    logger.info("Compute Score");
    int size = gmmList.size();
    logger.finer("GMM size:" + size);
    ArrayList<String> genderString = new ArrayList<String>();
    ArrayList<String> bandwidthString = new ArrayList<String>();
    for (int i = 0; i < size; i++) {
      String gmmName = gmmList.get(i).getName();
      if (parameter.getParameterScore().isGender() == true) {
        if (gmmName.equals("MS")) {
          genderString.add(Cluster.genderStrings[1]);
          bandwidthString.add(Segment.bandwidthStrings[2]);
        } else if (gmmName.equals("FS")) {
          genderString.add(Cluster.genderStrings[2]);
          bandwidthString.add(Segment.bandwidthStrings[2]);
        } else if (gmmName.equals("MT")) {
          genderString.add(Cluster.genderStrings[1]);
          bandwidthString.add(Segment.bandwidthStrings[1]);
        } else if (gmmName.equals("FT")) {
          genderString.add(Cluster.genderStrings[2]);
          bandwidthString.add(Segment.bandwidthStrings[1]);
        } else {
          genderString.add(Cluster.genderStrings[0]);
          bandwidthString.add(Segment.bandwidthStrings[0]);
        }
      } else {
        genderString.add(Cluster.genderStrings[0]);
        bandwidthString.add(Segment.bandwidthStrings[0]);
      }
    }

    ClusterSet clusterSetResult = new ClusterSet();
    for (Cluster cluster : clusterSet.clusterSetValue()) {
      double[] sumScoreVector = new double[size];
      int[] sumLenghtVector = new int[size];
      double ubmScore = 0.0;
      GMM gmmTop = null;
      if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
        gmmTop = gmmTopList.get(0);
      }
      Arrays.fill(sumScoreVector, 0.0);
      Arrays.fill(sumLenghtVector, 0);
      for (Segment currantSegment : cluster) {
        Segment segment = (currantSegment.clone());
        int end = segment.getStart() + segment.getLength();
        featureSet.setCurrentShow(segment.getShowName());
        double[] scoreVector = new double[size];
        double maxScore = 0.0;
        int idxMaxScore = 0;
        for (int i = 0; i < size; i++) {
          gmmList.get(i).score_initialize();
        }
        for (int start = segment.getStart(); start < end; start++) {
          for (int i = 0; i < size; i++) {
            GMM gmm = gmmList.get(i);
            if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
              if (i == 0) {
                gmmTop.score_getAndAccumulateAndFindTopComponents(
                    featureSet, start, parameter.getParameterTopGaussian().getScoreNTop());
              }
              gmm.score_getAndAccumulateForComponentSubset(
                  featureSet, start, gmmTop.getTopGaussianVector());
            } else {
              gmm.score_getAndAccumulate(featureSet, start);
            }
          }
        }

        if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
          ubmScore = gmmTop.score_getMeanLog();
          gmmTop.score_getSumLog();
          gmmTop.score_getCount();
          gmmTop.score_reset();
        }
        for (int i = 0; i < size; i++) {
          GMM gmm = gmmList.get(i);
          scoreVector[i] = gmm.score_getMeanLog();
          sumLenghtVector[i] += gmm.score_getCount();
          sumScoreVector[i] += gmm.score_getSumLog();
          if (i == 0) {
            maxScore = scoreVector[0];
            idxMaxScore = 0;
          } else {
            double value = scoreVector[i];
            if (maxScore < value) {
              maxScore = value;
              idxMaxScore = i;
            }
          }
          gmm.score_reset();
        }
        if (parameter.getParameterScore().isTNorm()) {
          double sumScore = 0;
          double sum2Score = 0;
          for (int i = 0; i < size; i++) {
            sumScore += scoreVector[i];
            sum2Score += (scoreVector[i] * scoreVector[i]);
          }
          for (int i = 0; i < size; i++) {
            double value = scoreVector[i];
            double mean = (sumScore - value) / (size - 1);
            double et = Math.sqrt(((sum2Score - (value * value)) / (size - 1)) - (mean * mean));
            scoreVector[i] = (value - mean) / et;
          }
        }
        if (parameter.getParameterScore().isGender() == true) {
          segment.setBandwidth(bandwidthString.get(idxMaxScore));
          segment.setInformation("segmentGender", genderString.get(idxMaxScore));
        }
        if (parameter.getParameterScore().isBySegment()) {
          for (int k = 0; k < size; k++) {
            double score = scoreVector[k];
            GMM gmm = gmmList.get(k);
            segment.setInformation("score:" + gmm.getName(), score);
            currantSegment.setInformation("score:" + gmm.getName(), score);
          }
          if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
            segment.setInformation("score:" + "UBM", ubmScore);
            currantSegment.setInformation("score:" + "UBM", ubmScore);
          }
        }
        String newName = cluster.getName();
        if (parameter.getParameterScore().isByCluster() == false) {
          if ((scoreVector[idxMaxScore] > parameter.getParameterSegmentation().getThreshold())
              && (parameter.getParameterScore().getLabel()
                  != ParameterScore.LabelType.LABEL_TYPE_NONE.ordinal())) {
            if (parameter.getParameterScore().getLabel()
                == ParameterScore.LabelType.LABEL_TYPE_ADD.ordinal()) {
              newName += "_";
              newName += gmmList.get(idxMaxScore).getName();
            } else {
              newName = gmmList.get(idxMaxScore).getName();
            }
          }

          Cluster temporaryCluster = clusterSetResult.getOrCreateANewCluster(newName);
          temporaryCluster.setGender(cluster.getGender());
          if (parameter.getParameterScore().isGender() == true) {
            temporaryCluster.setGender(genderString.get(idxMaxScore));
          }
          temporaryCluster.addSegment(segment);
        }
      }
      if (parameter.getParameterScore().isByCluster()) {
        for (int i = 0; i < size; i++) {
          sumScoreVector[i] /= sumLenghtVector[i];
        }
        if (parameter.getParameterScore().isTNorm()) {
          double sumScore = 0;
          double sum2Score = 0;
          for (int i = 0; i < size; i++) {
            sumScore += sumScoreVector[i];
            sum2Score += (sumScoreVector[i] * sumScoreVector[i]);
          }
          for (int i = 0; i < size; i++) {
            double value = sumScoreVector[i];
            double mean = (sumScore - value) / (size - 1);
            double et = Math.sqrt(((sum2Score - (value * value)) / (size - 1)) - (mean * mean));
            sumScoreVector[i] = (value - mean) / et;
          }
        }
        double maxScore = sumScoreVector[0];
        int idxMaxScore = 0;
        for (int i = 1; i < size; i++) {
          double s = sumScoreVector[i];
          if (maxScore < s) {
            maxScore = s;
            idxMaxScore = i;
          }
        }
        String newName = cluster.getName();
        if ((sumScoreVector[idxMaxScore] > parameter.getParameterSegmentation().getThreshold())
            && (parameter.getParameterScore().getLabel()
                != ParameterScore.LabelType.LABEL_TYPE_NONE.ordinal())) {
          if (parameter.getParameterScore().getLabel()
              == ParameterScore.LabelType.LABEL_TYPE_ADD.ordinal()) {
            newName += "_";
            newName += gmmList.get(idxMaxScore).getName();
          } else {
            newName = gmmList.get(idxMaxScore).getName();
          }
          // logger.finer("cluster name=" + cluster.getName() + " new_name=" + newName);
        }
        Cluster tempororaryCluster = clusterSetResult.getOrCreateANewCluster(newName);
        tempororaryCluster.setGender(cluster.getGender());
        if (parameter.getParameterScore().isGender() == true) {
          tempororaryCluster.setGender(genderString.get(idxMaxScore));
        }
        tempororaryCluster.setName(newName);
        for (Segment currantSegment : cluster) {
          Segment segment = (currantSegment.clone());
          if (parameter.getParameterScore().isGender() == true) {
            segment.setBandwidth(bandwidthString.get(idxMaxScore));
          }
          tempororaryCluster.addSegment(segment);
        }
        for (int k = 0; k < size; k++) {
          double score = sumScoreVector[k];
          GMM gmm = gmmList.get(k);
          // logger.finer("****clustername = " + newName + " name=" + gmm.getName() + " =" + score+"
          // k="+k);
          // logger.log(Level.SEVERE, "****clustername = " + newName + " name=" + gmm.getName() + "
          // =" + score);
          tempororaryCluster.setInformation("score:" + gmm.getName(), score);
          ClusterResult cr = new ClusterResult();
          cr.setName(newName);
          cr.getValue().put(score, gmm.getName());
          System.out.println(
              "------ clusterResultSet.putValue(newName, gmm.getName(), score)=----------------");
          System.out.println(newName + "  " + gmm.getName() + "  " + score);
          if (isName(gmm.getName())) {
            clusterResultSet.putValue(newName, gmm.getName(), score);
          } else {
            System.out.println("*****************" + gmm.getName() + " Non nome valido  ");
          }
        }

        if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
          // tempororaryCluster.putInformation("score:" + "length", ubmSumLen);
          // tempororaryCluster.putInformation("score:" + "UBM", ubmSumScore / ubmSumLen);
        }
      }
    }
    this.clusterSetResult = clusterSetResult;
    return clusterSetResult;
  }