Fix typos and grammatical errors

Author: jsingh811

paper/paper.md

@@ -44,12 +44,12 @@ It is an end-to-end solution for converting between audio file formats, building
 
 MATLAB is the language of choice for a vast amount of research in the audio and speech processing domain. On the contrary, Python remains the language of choice for a vast majority of Machine Learning research and functionality. This library contains features converted to Python that were originally built in MATLAB following a research invention. This software contributes to the available open-source software by enabling users to use Python based machine learning backend with highly researched audio features such as GFCC and others that are actively user for many audio classification based applications but are not readily available in Python due to primary popularity of research in MATLAB.  
 
-This software aims to provide machine learning engineers, data scientists, researchers and students with a set of baseline models to classify audio, the ability to use this library to build features on custom training data, the ability to automatically train on a scikit-learn classifier and perform hyper-parameter tuning using this library, the ability to export the built features for integration with any machine learning backend and the ability to classify audio files. This software furthers aims to aid users in addressing research efforts using GFCC and other evolving and actively researched audio features possible with Python.
+This software aims to provide machine learning engineers, data scientists, researchers and students with a set of baseline models to classify audio, the ability to use this library to build features on custom training data, the ability to automatically train on a scikit-learn classifier and perform hyper-parameter tuning using this library, the ability to export the built features for integration with any machine learning backend and the ability to classify audio files. This software further aims to aid users in addressing research efforts using GFCC and other evolving and actively researched audio features possible with Python.
 
 
 # Statement of need
 
-The motivation behind this software is understanding the popularity of Python for Machine Learning and presenting solutions for computing complex audio features using Python. This not only implies the need for resource to guide solutions for audio processing, but also signifies the need for Python guides and implementations to solve audio and speech classification tasks. The classifier implementation examples that are a part of this software and the README aim to give the users a sample solution to audio classification problems and help build the foundation to tackle new and unseen problems. 
+The motivation behind this software is understanding the popularity of Python for Machine Learning and presenting solutions for computing complex audio features using Python. This not only implies the need for resources to guide solutions for audio processing, but also signifies the need for Python guides and implementations to solve audio and speech classification tasks. The classifier implementation examples that are a part of this software and the README aim to give the users a sample solution to audio classification problems and help build the foundation to tackle new and unseen problems. 
 
 Different data processing techniques work well for different types of data. For example, word vector formations work great for text data [@nlp]. However, passing numbers data, an audio signal or an image through word vector formation is not likely to bring back any meaningful numerical representation that can be used to train machine learning models. Different data types correlate with feature formation techniques specific to their domain rather than a "one size fits all".
 
@@ -61,11 +61,11 @@ Some other popular libraries for the domain of audio processing include librosa
 
 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.
+The library further provides some pre-build audio classification models such as `speechVSmusic`, `speechVSmusicVSbirds` sound classifier and `music genre` classifier to 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.
 
 There is an additional functionality that allows users to convert their audio files to "wav" format to gain compatibility for using analysis and feature extraction on their audio files.
 
-Given the use of this software in the community today inspires the need and growth of this software. It is referenced in a text book titled `Artificial Intelligence with Python Cookbook` published by Packt Publishing in October 2020 [@packt]. Additionally, pyAudioProcessing is a part of specific admissions requirement for a funded PhD project at University of Portsmouth <sup id="portsmouth">[1](#footnote_portsmouth)</sup>. It is further referenced in this thesis paer titled "Master Thesis AI Methodologies for Processing Acoustic Signals AI Usage for Processing Acoustic Signals" [@phdthesis].
+Given the use of this software in the community today inspires the need and growth of this software. It is referenced in a text book titled `Artificial Intelligence with Python Cookbook` published by Packt Publishing in October 2020 [@packt]. Additionally, pyAudioProcessing is a part of specific admissions requirements for a funded PhD project at University of Portsmouth <sup id="portsmouth">[1](#footnote_portsmouth)</sup>. It is further referenced in this thesis paper titled "Master Thesis AI Methodologies for Processing Acoustic Signals AI Usage for Processing Acoustic Signals" [@phdthesis].
 
 <b id="footnote_portsmouth">1</b> https://www.port.ac.uk/study/postgraduate-research/research-degrees/phd/explore-our-projects/detection-of-emotional-states-from-speech-and-text [↩](#portsmouth)