diff --git a/pyAudioAnalysis/ShortTermFeatures.py b/pyAudioAnalysis/ShortTermFeatures.py
index 3a3bd4250..f79efa195 100644
--- a/pyAudioAnalysis/ShortTermFeatures.py
+++ b/pyAudioAnalysis/ShortTermFeatures.py
@@ -332,7 +332,7 @@ def chromagram(signal, sampling_rate, window, step, plot=False,
         sampling_rate:          the sampling freq (in Hz)
         window:         the short-term window size (in samples)
         step:        the short-term window step (in samples)
-        plot:        flag, 1 if results are to be ploted
+        plot:        flag, 1 if results are to be plotted
     RETURNS:
     """
     window = int(window)
@@ -397,7 +397,7 @@ def spectrogram(signal, sampling_rate, window, step, plot=False,
         sampling_rate:  the sampling freq (in Hz)
         window:         the short-term window size (in samples)
         step:           the short-term window step (in samples)
-        plot:           flag, 1 if results are to be ploted
+        plot:           flag, 1 if results are to be plotted
         show_progress flag for showing progress using tqdm
     RETURNS:
     """
diff --git a/pyAudioAnalysis/audioSegmentation.py b/pyAudioAnalysis/audioSegmentation.py
index aa718af42..956b5b7bc 100644
--- a/pyAudioAnalysis/audioSegmentation.py
+++ b/pyAudioAnalysis/audioSegmentation.py
@@ -682,10 +682,10 @@ def silence_removal(signal, sampling_rate, st_win, st_step, smooth_window=0.5,
          - signal:                the input audio signal
          - sampling_rate:               sampling freq
          - st_win, st_step:    window size and step in seconds
-         - smoothWindow:     (optinal) smooth window (in seconds)
-         - weight:           (optinal) weight factor (0 < weight < 1)
+         - smoothWindow:     (optional) smooth window (in seconds)
+         - weight:           (optional) weight factor (0 < weight < 1)
                               the higher, the more strict
-         - plot:             (optinal) True if results are to be plotted
+         - plot:             (optional) True if results are to be plotted
     RETURNS:
          - seg_limits:    list of segment limits in seconds (e.g [[0.1, 0.9],
                           [1.4, 3.0]] means that
diff --git a/pyAudioAnalysis/audioTrainTest.py b/pyAudioAnalysis/audioTrainTest.py
index 0913b656d..ece690a49 100644
--- a/pyAudioAnalysis/audioTrainTest.py
+++ b/pyAudioAnalysis/audioTrainTest.py
@@ -711,7 +711,7 @@ class i is [n_samples x numOfDimensions]
         # this is just for debugging (it should be equal to f1)
         f1_b = sklearn.metrics.f1_score(y_test_all, y_pred_all,
                                         average='macro')
-        # Note: np.mean(f1_per_exp) will not be exacty equal to the
+        # Note: np.mean(f1_per_exp) will not be exactly equal to the
         # overall f1 (i.e. f1 and f1_b because these are calculated on a
         # per-sample basis)
         f1_std = np.std(f1_per_exp)