Fix typo

Author: jsingh811

paper/paper.md

@@ -59,7 +59,7 @@ The library lets the user extract aggregated data features calculated per audio
 
 Some other popular libraries for the domain of audio processing include librosa [@mcfee2015librosa] and pyAudioAnalysis [@giannakopoulos2015pyaudioanalysis]. Librosa is a python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems. PyAudioAnalysis is a python library for audio feature extraction, classification, segmentation and applications. It allows the user to train scikit-learn models for mfcc, spectral and chroma features.
 
-PyAudioProcessing adds multiple additional features. The library includes the implementation of GFCC features converted from MATLAB based research to allow users to leverage Python with features for speech classification and speaker identification tasks in addition to MFCC and spectral features that are useful for music and other audio classification tasks. It allows the user to choose from the different feature options and use single or combinations of different audio features. The features can be run through a variety of scikit-learn models including a grid search for best model and Hyperparameters, along with a final confusion matrix and cross validation performance statistics. It further allows for saving and exporting the different audio features per audio file for the user to be able to leverage those while using a different custom classifier backend that is not a part of scikit-learn's models. 
+PyAudioProcessing adds multiple additional features. The library includes the implementation of GFCC features converted from MATLAB based research to allow users to leverage Python with features for speech classification and speaker identification tasks in addition to MFCC and spectral features that are useful for music and other audio classification tasks. It allows the user to choose from the different feature options and use single or combinations of different audio features. The features can be run through a variety of scikit-learn models including a grid search for best model and hyper-parameters, along with a final confusion matrix and cross validation performance statistics. It further allows for saving and exporting the different audio features per audio file for the user to be able to leverage those while using a different custom classifier backend that is not a part of scikit-learn's models. 
 
 The library further provides some pre-build audio classification models such as `speechVSmusic`, `speechVSmusicVSbirds` sound classifier and `music genre` classifier for give the users a baseline of pre-trained models for their common audio classification tasks. The user can use the library to build custom classifiers with the help of the instructions in the README.
 
@@ -72,7 +72,7 @@ Given the use of this software in the community today inspires the need and grow
 
 # Pre-trained models
 
-This software offer pre-trained models. This is an evolving feature as new datasets and classification problems gain prominence in research. Some of the pre-trained models include the following.
+This software offers pre-trained models. This is an evolving feature as new datasets and classification problems gain prominence in research. Some of the pre-trained models include the following.
 
 1. Audio type classifier to determine speech versus music: Trained SVM classifier for classifying audio into two possible classes - music, speech. This classifier was trained using MFCC, spectral and chroma features. Confusion matrix has scores such as follows.