diff --git a/utils/DER.py b/utils/DER.py
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+++ b/utils/DER.py
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+"""Calculates Diarization Error Rate (DER) which is the sum of Missed Speaker (MS),
+False Alarm (FA), and Speaker Error Rate (SER) using md-eval-22.pl from NIST RT Evaluation.
+
+Authors
+ * Neville Ryant 2018
+ * Nauman Dawalatabad 2020
+
+Credits
+ This code is adapted from https://github.com/nryant/dscore
+"""
+
+import os
+import re
+import subprocess
+import numpy as np
+
+FILE_IDS = re.compile(r"(?<=Speaker Diarization for).+(?=\*\*\*)")
+SCORED_SPEAKER_TIME = re.compile(r"(?<=SCORED SPEAKER TIME =)[\d.]+")
+MISS_SPEAKER_TIME = re.compile(r"(?<=MISSED SPEAKER TIME =)[\d.]+")
+FA_SPEAKER_TIME = re.compile(r"(?<=FALARM SPEAKER TIME =)[\d.]+")
+ERROR_SPEAKER_TIME = re.compile(r"(?<=SPEAKER ERROR TIME =)[\d.]+")
+
+
+def rectify(arr):
+    """Corrects corner cases and converts scores into percentage.
+    """
+
+    # Numerator and denominator both 0.
+    arr[np.isnan(arr)] = 0
+
+    # Numerator > 0, but denominator = 0.
+    arr[np.isinf(arr)] = 1
+    arr *= 100.0
+
+    return arr
+
+
+def DER(
+    ref_rttm,
+    sys_rttm,
+    ignore_overlap=False,
+    collar=0.25,
+    individual_file_scores=False,
+):
+    """Computes Missed Speaker percentage (MS), False Alarm (FA),
+    Speaker Error Rate (SER), and Diarization Error Rate (DER).
+
+    Arguments
+    ---------
+    ref_rttm : str
+        The path of reference/groundtruth RTTM file.
+    sys_rttm : str
+        The path of the system generated RTTM file.
+    individual_file : bool
+        If True, returns scores for each file in order.
+    collar : float
+        Forgiveness collar.
+    ignore_overlap : bool
+        If True, ignores overlapping speech during evaluation.
+
+    Returns
+    -------
+    MS : float array
+        Missed Speech.
+    FA : float array
+        False Alarms.
+    SER : float array
+        Speaker Error Rates.
+    DER : float array
+        Diarization Error Rates.
+
+    Example
+    -------
+    >>> import pytest
+    >>> pytest.skip('Skipping because of Perl dependency')
+    >>> ref_rttm = "../../samples/rttm_samples/ref_rttm/ES2014c.rttm"
+    >>> sys_rttm = "../../samples/rttm_samples/sys_rttm/ES2014c.rttm"
+    >>> ignore_overlap = True
+    >>> collar = 0.25
+    >>> individual_file_scores = True
+    >>> Scores = DER(ref_rttm, sys_rttm, ignore_overlap, collar, individual_file_scores)
+    >>> print (Scores)
+    (array([0., 0.]), array([0., 0.]), array([7.16923618, 7.16923618]), array([7.16923618, 7.16923618]))
+    """
+
+    curr = os.path.abspath(os.path.dirname(__file__))
+    mdEval = os.path.join(curr, "../../tools/der_eval/md-eval.pl")
+
+    cmd = [
+        mdEval,
+        "-af",
+        "-r",
+        ref_rttm,
+        "-s",
+        sys_rttm,
+        "-c",
+        str(collar),
+    ]
+    if ignore_overlap:
+        cmd.append("-1")
+
+    try:
+        stdout = subprocess.check_output(cmd, stderr=subprocess.STDOUT)
+
+    except subprocess.CalledProcessError as ex:
+        stdout = ex.output
+
+    else:
+        stdout = stdout.decode("utf-8")
+
+        # Get all recording IDs
+        file_ids = [m.strip() for m in FILE_IDS.findall(stdout)]
+        file_ids = [
+            file_id[2:] if file_id.startswith("f=") else file_id
+            for file_id in file_ids
+        ]
+
+        scored_speaker_times = np.array(
+            [float(m) for m in SCORED_SPEAKER_TIME.findall(stdout)]
+        )
+
+        miss_speaker_times = np.array(
+            [float(m) for m in MISS_SPEAKER_TIME.findall(stdout)]
+        )
+
+        fa_speaker_times = np.array(
+            [float(m) for m in FA_SPEAKER_TIME.findall(stdout)]
+        )
+
+        error_speaker_times = np.array(
+            [float(m) for m in ERROR_SPEAKER_TIME.findall(stdout)]
+        )
+
+        with np.errstate(invalid="ignore", divide="ignore"):
+            tot_error_times = (
+                miss_speaker_times + fa_speaker_times + error_speaker_times
+            )
+            miss_speaker_frac = miss_speaker_times / scored_speaker_times
+            fa_speaker_frac = fa_speaker_times / scored_speaker_times
+            sers_frac = error_speaker_times / scored_speaker_times
+            ders_frac = tot_error_times / scored_speaker_times
+
+        # Values in percentage of scored_speaker_time
+        miss_speaker = rectify(miss_speaker_frac)
+        fa_speaker = rectify(fa_speaker_frac)
+        sers = rectify(sers_frac)
+        ders = rectify(ders_frac)
+
+        if individual_file_scores:
+            return miss_speaker, fa_speaker, sers, ders
+        else:
+            return miss_speaker[-1], fa_speaker[-1], sers[-1], ders[-1]