Merge pull request #91 from Moriango/master

Fixed spelling mistakes

Author: DavidDoukhan

README.md

@@ -9,7 +9,7 @@ inaSpeechSegmenter is a CNN-based audio segmentation toolkit suited to the tasks
 
 It splits audio signals into homogeneous zones of speech, music and noise.
 Speech zones are split into segments tagged using speaker gender (male or female).
-Male and female classification models are optimized for French language since they were trained using French speakers (accoustic correlates of speaker gender are language dependent).
+Male and female classification models are optimized for French language since they were trained using French speakers (acoustic correlates of speaker gender are language dependent).
 Zones corresponding to speech over music or speech over noise are tagged as speech.
 Singing voice is tagged as music.
 
@@ -40,7 +40,7 @@ $ sudo apt-get install ffmpeg
 
 ### PIP installation
 ```bash
-# create a python 3 virtual environement and activate it
+# create a python 3 virtual environment and activate it
 $ virtualenv -p python3 env
 $ source env/bin/activate
 # install framework and dependencies
@@ -52,7 +52,7 @@ $ pip install inaSpeechSegmenter
 ```bash
 # clone git repository
 $ git clone https://github.com/ina-foss/inaSpeechSegmenter.git
-# create a python 3 virtual environement and activate it
+# create a python 3 virtual environment and activate it
 $ virtualenv -p python3 env
 $ source env/bin/activate
 # install framework and dependencies
@@ -105,7 +105,7 @@ inaSpeechSegmenter has been presented at the IEEE International Conference on Ac
 ```
 
 inaSpeechSegmenter won [MIREX 2018 speech detection challenge](http://www.music-ir.org/mirex/wiki/2018:Music_and_or_Speech_Detection_Results)
-Details on the speech detection submodule can be found bellow:
+Details on the speech detection submodule can be found below:
 
 ```bibtex
 @inproceedings{ddoukhanmirex2018,

inaSpeechSegmenter/sidekit_mfcc.py

@@ -314,8 +314,8 @@ def mfcc(input_sig,
         
             - Pre-processing in time-domain (pre-emphasizing)
             - Compute the spectrum amplitude by windowing with a Hamming window
-            - Filter the signal in the spectral domain with a triangular filter-bank, whose filters are approximatively
-               linearly spaced on the mel scale, and have equal bandwith in the mel scale
+            - Filter the signal in the spectral domain with a triangular filter-bank, whose filters are approximately
+               linearly spaced on the mel scale, and have equal bandwidth in the mel scale
             - Compute the DCT of the log-spectrom
             - Log-energy is returned as first coefficient of the feature vector.
     

scripts/ina_speech_segmenter.py

@@ -36,7 +36,7 @@
 description = """Do Speech/Music(/Noise) and Male/Female segmentation and store segmentations into CSV files. Segments labelled 'noEnergy' are discarded from music, noise, speech and gender analysis. 'speech', 'male' and 'female' labels include speech over music and speech over noise. 'music' and 'noise' labels are pure segments that are not supposed to contain speech.
 """
 epilog="""
-Detailled description of this framework is presented in the following study:
+Detailed description of this framework is presented in the following study:
 Doukhan, D., Carrive, J., Vallet, F., Larcher, A., & Meignier, S. (2018, April). An open-source speaker gender detection framework for monitoring gender equality. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5214-5218). IEEE.
 """