diff --git a/acoustic_model/check_novoapi.py b/acoustic_model/check_novoapi.py
index 3fd4601..178706b 100644
--- a/acoustic_model/check_novoapi.py
+++ b/acoustic_model/check_novoapi.py
@@ -21,8 +21,11 @@ from forced_alignment import pyhtk, convert_phone_set
 import novoapi 
 import novoapi_functions
 
-## ======================= novo phoneset ======================
-phoneset_ipa, phoneset_novo70, translation_key = novoapi_functions.load_phonset()
+## ===== load novo phoneset =====
+phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = novoapi_functions.load_phonset()
+
+
+## ===== extract pronunciations written in novo70 only (not_in_novo70) =====
 
 # As per Nederlandse phoneset_aki.xlsx recieved from David
 # [ɔː] oh / ohr
@@ -33,10 +36,7 @@ phoneset_ipa, phoneset_novo70, translation_key = novoapi_functions.load_phonset(
 # [w] wv in IPA written as ʋ. 
 david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']
 
-
-## ======================= extract words which is written only with novo70 ======================
-mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
-
+## read pronunciation variants.
 stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
 df = pd.read_excel(stimmen_transcription_, 'frequency')
 #for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
@@ -68,7 +68,8 @@ for ipa in transcription_ipa:
 	not_in_novo70.extend(not_in_novo70_)
 not_in_novo70_list = list(set(not_in_novo70))
 
-## check which phone is used in stimmen but not in novo70
+
+## check which phones used in stimmen but not in novo70
 # 'ʀ', 'ʁ',
 # 'ɒ', 'ɐ', 
 # 'o', 'a' (o:, a:?)
@@ -92,10 +93,12 @@ def search_phone_ipa(x, phone_list):
 #search_phone_ipa('ø', transcription_ipa)
 
 
-
-
+## ===== load all transcriptions (df) =====
 df = pd.read_excel(stimmen_transcription_, 'original')
 
+# mapping from ipa to xsampa
+mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
+
 ipas     = []
 famehtks = []
 for xsampa in df['Self Xsampa']:
@@ -117,15 +120,11 @@ df = pd.DataFrame({'filename': df['Filename'],
                     'xsampa': df['Self Xsampa'],
                     'ipa': pd.Series(ipas)})
 
-# find options which all phones are in novo70.
-#word_list = list(set(df['word']))
-#word_list = [word for word in word_list if not pd.isnull(word)]
-#word = word_list[1]
+word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
+word_list = sorted(word_list)
 
-## pronunciation variants of 'word' 
-#df_ = df[df['word'] == word]['xsampa']
-##pronunciation_variant = list(set(df_))
 
+## check frequency of each pronunciation variants
 cols = ['word', 'ipa', 'frequency']
 df_samples = pd.DataFrame(index=[], columns=cols)
 for ipa in all_in_novo70:
@@ -133,4 +132,13 @@ for ipa in all_in_novo70:
 	samples = df[df['ipa'] == ipa]
 	word = list(set(samples['word']))[0]
 	samples_Series = pd.Series([word, ipa, len(samples)], index=df_samples.columns)
-	df_samples = df_samples.append(samples_Series, ignore_index=True)
\ No newline at end of file
+	df_samples = df_samples.append(samples_Series, ignore_index=True)
+
+# each word
+df_per_word = pd.DataFrame(index=[], columns=df_samples.keys())
+
+for word in word_list:
+	df_samples_ = df_samples[df_samples['word']==word]
+	df_samples_ = df_samples_[df_samples_['frequency']>1]
+	df_per_word = df_per_word.append(df_samples_, ignore_index=True)
+df_per_word.to_excel(os.path.join(default.stimmen_dir, 'pronunciation_variants_novo70.xlsx'), encoding="utf-8")
\ No newline at end of file
diff --git a/acoustic_model/novoapi_functions.py b/acoustic_model/novoapi_functions.py
index 0ab6aa8..bbf44ff 100644
--- a/acoustic_model/novoapi_functions.py
+++ b/acoustic_model/novoapi_functions.py
@@ -39,7 +39,7 @@ def load_phonset():
 	phoneset_ipa    = np.unique(phoneset_ipa)
 	phoneset_novo70 = np.unique(phoneset_novo70)
 
-	return phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa
+	return 
 
 
 def multi_character_tokenize(line, multi_character_tokens):