def METHOD_NAME(file_list, gt_file_list, args, MCD):
    for i, cvt_path in enumerate(file_list):
        corresponding_list = list(
            filter(lambda gt_path: get_basename(gt_path) in cvt_path, gt_file_list)
        )
        assert len(corresponding_list) == 1
        gt_path = corresponding_list[0]
        gt_basename = get_basename(gt_path)
        # extract ground truth and converted features
        gt_feats = world_extract(gt_path, args)
        cvt_feats = world_extract(cvt_path, args)
        # VAD & DTW based on power
        gt_mcep_nonsil_pow = extfrm(gt_feats["mcep"], gt_feats["npow"])
        cvt_mcep_nonsil_pow = extfrm(cvt_feats["mcep"], cvt_feats["npow"])
        _, path = fastdtw(
            cvt_mcep_nonsil_pow,
            gt_mcep_nonsil_pow,
            dist=scipy.spatial.distance.euclidean,
        )
        twf_pow = np.array(path).T
        # MCD using power-based DTW
        cvt_mcep_dtw_pow = cvt_mcep_nonsil_pow[twf_pow[0]]
        gt_mcep_dtw_pow = gt_mcep_nonsil_pow[twf_pow[1]]
        diff2sum = np.sum((cvt_mcep_dtw_pow - gt_mcep_dtw_pow) ** 2, 1)
        mcd = np.mean(10.0 / np.log(10.0) * np.sqrt(2 * diff2sum), 0)
        print("{} {}".format(gt_basename, mcd))
        MCD.append(mcd)