Speaker Recognition System Pattern Classification

Speaker Recognition System Pattern Classification A Study on Speaker Recognition System and Pattern classification Techniques Dr E.Chandra,  K.Manikandan,  M.S.Kalaivani Abstract Speaker Recognition is the process of identifying a person through his/her voice signals or speech waves. Pattern classification plays a vital role in speaker recognition. Pattern classification is the process of grouping the patterns, which are sharing the same set of properties. This paper deals with speaker recognition system and over view of Pattern classification techniques DTW, GMM and SVM. Keywords Speaker Recognition System, Dynamic Time Warping (DTW), Gaussian Mixture Model (GMM), Support Vector Machine (SVM). INTRODUCTION Speaker Recognition is the process of identifying a person through his/her voice signals [1] or speech waves. It can be classified into two categories, speaker identification and speaker verification. In speaker identification task, a speech utterance of an unknown speaker is compared with set of valid users. The best match is used to identify the speaker. Similarly, in speaker verification the unknown speaker first claims identity, and the claimed model is then used for identification. If the match is above a predefined threshold, the identity claim is accepted The speech used for these task can be either text dependent or text independent. In text dependent application the system has the prior knowledge of the text to be spoken. The user will speak the same text as it is in the predefined text. In a text-independent application, there is no prior knowledge by the system of the text to be spoken. Pattern classification plays a vital role in speaker recognition. The term Pattern defines the objects of interest. In this paper the sequence of acoustic vectors, extracted from input speech are taken as patterns. Pattern classification is the process of grouping the patterns, which are sharing the same set of properties. It plays a vital role in speaker recognition system. The result of pattern classification decides whether to accept or reject a speaker. Several research efforts have been done in pattern classification. Most of the works based on generative model. There are Dynamic Time Warping (DTW) [3], Hidden Markov Models (HMM) , Vector Quantization (VQ) [4], Gaussian mixture model (GMM) [5] and so forth. Generative model is for randomly generating observed data, with some hidden parameters. Because of the randomly generating observed data functions, they are not able to provide a machine that can directly optimize discrimination. Support vector machine was introducing as an alternative classifier for speaker verification. [6]. In machine learning SVM is a new tool, which is used for hard classification problems in several fields of application. This tool is capable to deal with the samples of higher dimensionality. In speaker verification binary decision is needed, since SVM is discriminative binary classifier it can classify a complete utterance in a single step. This paper is planned as follows. In section 2: speaker recognition system, in section 3, Pattern Classification, AND overview of DTW, GMM, and SVM techniques .section 4: Conclusion. SPEAKER RECOGNITION SYSTEM Speaker recognition categorized into verification and identification. Speaker Recognition system consists of two stages .speaker verification and speaker identification. Speaker verification is 1:1 match, where the voice print is matched with one template. But speaker identification is 1:N match, where the input speech is matched with more than one templates. Speaker verification consists of five steps. 1. Input data acquisition 2.feature extraction 3.pattern matching 4.decision making 5.generate speaker models. Fig 1: Speaker recognition system In the first step sample speech is acquired in a controlled manner from the user. The speaker recognition system will process the speech signals and extract the speaker discriminatory information. This information forms a speaker model. At the time of verification process, a sample voice print is acquired from the user. The speaker recognition system will extract the features from the input speech and compared withpredefined model. This process is called pattern matching. DC Offset Removal and Silence Removal Speech data are discrete-time speech signals, carry some redundant constant offset called DC offset [8].The values of DC offset affect the information ,extracted from the speech signals. Silence frames are audio frames of background noise with low energy level .silence removal is the process of discarding the silence period from the speech. The signal energy in each speech frame is calculated by using equation (1). M – Number of samples in a speech frames, N- Total number of speech frames. Threshold level is determined by using the equation (2) Threshold = Emin + 0.1 (Emax – Emin) (2) Emax and Emin are the lowest and greatest values of the N segments. Fig 2. Speech Signal before Silence Removal Fig 3. Speech Signal after Silence Removal This technique is used to enhance the high frequencies of the speech signal. The aim of this technique is to spectrally flatten the speech signal that is to increase the relative energy of its high frequency spectrum. The following two factors decides the need of Pre-emphasis technique.1.Speech Signals generally contains more speaker specific information in higher frequencies [9]. 2. If the speech signal energy decreases the frequency increases .This made the feature extraction process to focus all the aspects of the voice signals. Pre-emphasis is implemented as first order finite Impulse Response filter, defined as H(Z) = 1-0.95 Z-1 (3) The below example represents speech signals before and after Pre-emphasizing. Fig 4. Speech Signal before Pre-emphasizing Fig 5. Speech Signal after Pre-emphasizing Windowing and Feature Extraction: The technique windowing is used to minimize the signal discontinuities at beginning and end of each frame. It is used to smooth the signal and makes the frame more flexible for spectral analysis. The following equation is used in windowing technique. y1(n) = x (n)w(n), 0 ≠¤Ãƒ ¯Ã¢â€š ¬Ã‚  n ≠¤Ãƒ ¯Ã¢â€š ¬Ã‚  N-1 (4) N- Number of samples in each frame. The equation for Hamming window is(5) There is large variability in the speech signal, which are taken for processing. to reduce this variability ,feature extraction technique is needed. MFCC has been widely used as the feature extraction technique for automatic speaker recognition. Davis and Mermelstein reported that Mel-frequency cepstral Coefficients (MFCC) provided better performance than other features in 1980 [10]. Fig 6. Feature Extraction MFCC technique divides the input signal into short frames and apply the windowing techniques, to discard the discontinuities at edges of the frames. In fast Fourier transform (FFT) phase, it converts the signal to frequency domain and after that Mel scale filter bank is applied to the resultant frames. After that, Logarithm of the signal is passed to the inverse DFT function converting the signal back to time domain. PATTERN CLASSIFICATION Pattern classification involves in computing a match score in speaker recognition system. The term match score refers the similarity of the input feature vectors to some model. Speaker models are built from the features extracted from the speech signal. Based on the feature extraction a model of the voice is generated and stored in the speaker recognition system. To validate a user the matching algorithm compares the input voice signal with the model of the claimed user. In this paper three techniques in pattern classification have been compared. Those three major techniques are DTW, GMM and SVM. Dynamic Time Warping: This well known algorithm is used in many areas. It is currently used in Speech recognition,sign language recognition and gestures recognition, handwriting and online signature matching ,data mining and time series clustering, surveillance , protein sequence alignment and chemical engineering , music and signal processing . Dynamic Time Warping algorithm is proposed by Sadaoki Furui in 1981.This algorithm measures the similarity between two series which may vary in time and speed. This algorithm finds an optimal match between two given sequences. The average of the two patterns is taken to form a new template. This process is repeated until all the training utterances have been combined into a single template. This technique matches a test input from a multi-dimensional feature vector T= [ t1, t2†¦tI] with a reference template R= [ r1, r2†¦rj]. It finds the function w(i) as shown in the below figure. In Speaker Recognition system Every input speech is compared with the utte rance in the database .For each comparison, the distance measure is calculated .In the measurements lower distance indicates higher similarity. Fig 7. . Dynamic Time Warping Gaussian mixture model: Gaussian mixture model is the most commonly used classifier in speaker recognition system.It is a type of density model which comprises a number of component functions. These functions are combined to provide a multimodal density. This model is often used for data clustering. It uses an alternative algorithm that converges to a local optimum. In this method the distribution of the feature vector x is modeled clearly using mixture of M Gaussians. mui- represent the mean and covariance of the i th mixture. x1, x2†¦xn, Training data ,M-number of mixture. The task is parameter estimation which best matches the distribution of the training feature vectors given in the input speech. The well known method is maximum likehood estimation. It finds the model parameters which maximize the likehood of GMM. Therefore, the testing data which gain a maximum score will recognize as speaker. Support Vector Machine: Support machine was proposed in 1990 and it is one of the best machine learning algorithms. This is used in many pattern classification problems. such as image recognition, speech recognition, text categorization, face detection and faulty card detection, etc. The basic idea of support vector machine is to find the optimal linear decision surface based on the concept of structural risk minimization. It is a binary classification method. The decision surface refers the weighted combination of elements in a training dataset. These elements are called support vectors. These vectors define the boundary between two classes. In a binary problem +1 and -1 are taken as two classes. The size of the margin should be maximized to characterize the boundary between two classes. The below example explains pattern classification by using SVM. In the fig 3(a), there are two different kinds of patterns taken for process. A line is drawn to separate these two patterns. In the fig 3(b),by using a single line the patterns are separated, the patterns are presented in two dimensional space. The similar representation in one dimensional space in the fig 3(c), a point can be used to separate patterns in one dimensional space. a plane that separates these patterns in 3-D space ,represented in the fig 3(d),is called separating hyper plane. . The next task a plane should be selected from the set of planes whose margin is maximum. The plane with the maximum margin i.e. perpendicular distance from the marginal line is known as optimal hyper plane or maximum margin hyper plane as shown in fig 3(f). The patterns that lie on the edges of the plane are called support vectors While classify the patterns, there may exist some errors in the representation, as shown in the fig 3(g), such types of errors are called soft margin. Sometimes ,these errors can be ignored to some threshold value. The patterns that can be easily separated using line or Plane are called linearly Separable patterns .Non-linear separable patterns (fig-j,k,l)are difficult to classify. These patterns are classified by using kernel functions . In order to classify non-linear separable patterns the original data’s are mapped to higher dimensional space using kernel function. CONCLUSION In this paper we have explained about speaker recognition system and discussed about three major pattern classification techniques, Dynamic Time Warping, Gaussian mixture model and Support Vector Machine. SVM will work efficiently on fixed length vectors. To implement SVM the input data should be normalized for better performance. In future, we have planned to implement these techniques in speaker recognition system and evaluate the performance. The performance of the models will also be evaluated by incrementing the amounts of training data. REFERENCES [1] Campbell, J.P., Speaker Recognition: A Tutorial, Proc. Of the IEEE, vol. 85,no. 9, 1997, pp. 1437-1462. [2] Sadaoki Furui., Recent advances in speaker recognition,Pattern Recognition Letters. 1997,18 (9): 859-72. [3] Sakoe, H.and Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, Acoustics,Speech, and Signal Processing, IEEE Transactions on Volume 26, Issue 1, Feb 1978 Page 43 49. [4] Lubkin, J. and Cauwenberghs, G., VLSI Implementation of Fuzzy Adaptive Resonance and Learning Vector Quantization, Int. J. Analog Integrated Circuits and Signal Processing, vol. 30 (2), 2002,pp. 149-157. [5] Reynolds, D. A. and Rose, R. C. Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans. Speech Audio Process. 3, 1995, pp 72–83. [6] Solera, U.R., Martà ­n-Iglesias, D., Gallardo-Antolà ­n, A., Pelà ¡ez-Moreno, C. and Dà ­az-de-Marà ­a, F, Robust ASR using Support Vector Machines, Speech Communication, Volume 49 Issue 4, 2007. [7] Temko, A.; Monte, E.; Nadeu, C., Comparison of Sequence Discriminant Support Vector Machines for Acoustic Event Classification, ICASSP 2006 Proceedings, 2006 IEEE International Conference on Volume 5, Issue , 14-19 May 2006 [8] Shang, S.; Mirabbasi, S.; Saleh, R., A technique for DCoffset removal and carrier phase error compensation in integrated wireless receivers Circuits and Systems, ISCAS apos;03. Proceedings of the 2003 International Symposium onVolume 1, Issue , 25-28 May 2003 Page I-173 I-176 vol.1 [9] Vergin, R.; Oapos;Shaughnessy, D., Pre-emphasis and speech recognition lectrical and Computer Engineering†,Canadian Conference on Volume 2, Issue , 5-8 Sep 1995 [10] Davis, S. B. and Mermelstein, P., Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, IEEE Trans. on Acoustic, Speech and Signal Processing, ASSP-28, 1980, No. 4. [11] Sadaoki Furui., Cepstral analysis technique for automatic speaker verification, IEEE Trans. ASSP 29, 1981,pages 254-272. BIOGRAPHIES Dr.E.Chandra received her B.Sc., from Bharathiar University, Coimbatore in 1992 and received M.Sc., from Avinashilingam University ,Coimbatore in 1994. She obtained her M.Phil. In the area of Neural Networks from Bharathiar University, in 1999. She obtained her PhD degree in the area of Speech recognition system from Alagappa University Karikudi in 2007. She has totally 15 yrs of experience in teaching including 6 months in the industry. Presently she is working as Director, Department of Computer Applications in D. J. Academy for Managerial Excellence, Coimbatore. She has published more than 30 research papers in National, International Journals and Conferences in India and abroad. She has guided more than 20 M.Phil. Research Scholars. Currently 3 M.Phil Scholars and 8 PhD Scholars are working under her guidance. She has delivered lectures to various Colleges. She is a Board of studies member of various Institutions. Her research interest lies in the area of Data Mining, Artificial Intelligence, Neural Networks, Speech Recognition Systems, Fuzzy Logic and Machine Learning Techniques. She is an active and Life member of CSI, Society of Statistics and Computer Applications. Currently she is Management Committee member of CSI Coimbatore Chapter. K. Manikandan received his Bsc from Bharathidhasan University, Tiruchirappalli in1998 and received his MCA from Bharathiadsan University, Tiruchirappalli in 2001. He received M.Phil in the area of soft computing from Bharathiyar university, Coimbatore in 2004. He has 12 years of experience in teaching. Currently, he is working as a Assistant Professor, Department Of Computer Science, PSG College of arts and Science, Coimbatore and pursuing PhD in Bharathiar University, Coimbatore.He has presented research papers in National and International Conferences and published a paper in International Journal. His Research Interest is Soft Computing . He is Life a member of IAENG. He has guided more than 4 M.Phil Research Scholars. Currently 3 M.Phil Scholars are working under his guidance. He has delivered lectures to various Colleges. M.S.Kalaivani received her BCA from P.S.G College of Arts and Science, Coimbatore, in 2005 and received her MCA from National Institute of Technology, Tiruchirappalli in 2008.She has 4 years of working experience at software industry. Presently, she is working as a Research Scholar, Department of Computer Science, P.S.G. College of Arts and Science, Coimbatore. Her research interests are Machine Learning and Fuzzy logic.

Article on How Teenagers Spend Their Free Time

What do teenagers do in their spare time? They are on Facebook! This is Bristol FollowWednesday, November 10, 2010 WHAT do we teenagers do in our spare time? Hang round by shops with our hoods up, knifes in pocket, shouting abuse and getting drunk? Yes, all those grown ups would like to think that but, we normally just go out to have fun – go to the cinema, shopping and places like that! Or we are on Facebook. We will spend our lives, sharing our lives, on Facebook. From posting pictures, writing statuses, and joining funny yet sometimes offensive groups or fan pages.But, have you ever thought, that maybe, you could get into heaps of trouble from those little comments? Facebook can get quite abusive. With us posting pictures, which, yes, aren't very modest of ourselves, with skimpy outfits, and slapped-on make-up. But the nasty comments, are where we draw the line. Consider this, once it's on there, will your digital footprint ever be erased? Imogen Rodgers, Lucy Perry, Bethan y Seymour, Year 9, St Bede's CAN Facebook fight to stay on our favourites or is it time to ignore the friend you don't like?It's most people's way of life – an addiction. With no less than 400 million active users Facebook is ranked the number one social networking site worldwide but with so many others, like Twitter, Bebo, MySpace, Flickr, Google buzz, Habbo, Friendster, the list is endless, Will Facebook keep its crown or will one of its enemies take over? At the moment 35 million Facebook users update their status each day, this shows how popular it is. The site's publicity and popularity levels are soaring, but over the past four years another site has had quite a bit of the spotlight too.Twitter was launched in 2006 and with its appeal of getting to hear what celebrities have to say directly from them and reading their every â€Å"tweet† it seemed like there was a new social fish in town. However, even though it may seem like Facebook is starting to slip away, no other site could take over from what stole our hearts first. Elsie Bradley, The City Academy, Bristol DO you have Facebook? Some people feel that entertainment is sitting in front of a computer and watching the lives of others dissolve into this new cyber life.How many times have you seen a message saying â€Å"I'm bored! â€Å"? They make me want to scream, â€Å"THEN GO AND DO SOMETHING ELSE! † But the truth is that our lives are now revolving around these social networking sites. Those who don't have such groups as Facebook are constantly under peer pressure to create an account and get sucked in. Especially if you don't have an account, you have no control whatsoever of what pictures of you are being pinned up on the internet.All those pictures that you thought your â€Å"friends† deleted – they're all up on Facebook. Sarah Orr, Amy McGrath, and Evie Gowie, Year 9, St Bede's I, LIKE many other people really enjoy going home and using the computer maybe to play games, send an email or go on Facebook but are we getting too addicted to Facebook? Do you really need to go on Facebook, when you have just spent a day at school talking to those people? Emily Shiga, Banor Kofi-Ofuafor and Jess Chapman, Year 9, St Bede's

The Toyota Camry Hybrid And The Camry Sedan

Now a day’s the car becomes more important than needs. People now want the pretty and high quality models of new cars. The demand is increasing for new model car, so company every year make the new model cars. The Camry hybrid and the Camry sedan are manufactured by Toyota since 1982. The Camry hybrid and the Camry sedan is a Japanese car. These cars are the best-selling cars in North America and also sell in Australia very well. The Camry Hybrid and the Camry Sedan has been reshaped for 2013 and represents the seventh-generation model.The present Toyota Camry interior is very pretty than the past Camry’s, and it is the four-cylinder engine is additional powerful. On other hand, The Toyota Camry Hybrid of 2013 is understated about its fuel efficiency. It offers both impatient speeding up and great fuel economy. Camry Hybrid drives Zero to 60 in just 7. 4 seconds, it is not sports car, but it’s faster than the regular four-cylinder Camry. The Toyota Camry Hybrid a nd the Camry Sedan of 2013 is offered in XLE and LE trim levels.The LE features 16-inch steel wheels, automatic headlights, keyless ignition/entry, full power accessories, dual-zone automatic climate control, cruise control, a trip computer, a tilt-and-telescoping steering wheel, Bluetooth phone and audio connectivity, a 6-inch display and a six-speaker sound system with a HD radio, CD player, , an auxiliary input, satellite radio and a USB/iPod interface. The XLE adds heated exterior mirrors, 17-inch alloy wheels, a leather-wrapped steering wheel. Three engines were presented for this generation.The first was a 2. 4-liter four-cylinder that complete 154 hp (145 with PZEV emissions controls). It was reproduced to either a five-speed manual or a five-speed automatic transmission (four-speed prior to '05) and must be powerful sufficient for the common of buyers. A 3. 0-liter V6 that made 190 hp was also offered (18 hp less prior to '04) on the LE and XLE trim levels, while a 210-hp, 3 . 3-liter V6 (introduced for 2004) was offered on the SE model only. These six-cylinder Camry’s came by the automatic only.In preceding years, these power numbers were greater for the reason that of a change in measurement that happened in 2006, while actual output not once changed. The Toyota Camry Hybrid of 2013 is animatedly accomplished. Thanks a lot to careful suspension tuning, the position of car during driving is sticks fit to the road as well as is generally untouched by bumps and ruts. The electric-assist power steering is soft and quick turning, however it suffers from a lack of comment and some drivers might be catch its effort too graceful.

5 Ways to Fix the Comma Splice

5 Ways to Fix the Comma Splice 5 Ways to Fix the Comma Splice 5 Ways to Fix the Comma Splice By Mark Nichol A comma splice is simply a sentence in which a comma is called on to do more than is appropriate for the workaday but weak punctuation mark. When a sentence contains two independent clauses each of which could essentially stand on its own separated by a comma (or by nothing at all, in which case it’s called a fused sentence), employ one of these five strategies to fix the splice and create a correct connection: 1. â€Å"Of course not all companies will survive, it is our goal to give the investing public accurate information on all companies profiled.† Divide the sentence into two (and set â€Å"Of course† off with a comma as well): â€Å"Of course, not all companies will survive. It is our goal to give the investing public accurate information on all companies profiled.† 2. â€Å"Some buildings hearken back to Main Street, USA, others offer strip-mall modernism.† Insert a subordinating conjunction to convert either clause into a subordinate clause (one that depends on the other to be the main clause): â€Å"Some buildings hearken back to Main Street, USA, while others offer strip mall modernism.† (While could, alternatively, begin the sentence.) 3. â€Å"Several people have told me they want to buy a house before they are laid off, otherwise they won’t be able to get a loan.† Replace the comma with a semicolon (and, in this case, set otherwise off from the rest of the second clause: â€Å"Several people have told me they want to buy a house before they are laid off; otherwise, they won’t be able to get a loan.† 4. â€Å"At times, it resembled the pitch of a whirring blender, at other moments, an angelic choir.† Separate the clauses with a coordinating conjunction: â€Å"At times, it resembled the pitch of a whirring blender, and at other moments, an angelic choir.† (The final comma and the elided phrase â€Å"an angelic choir† are correct; repetition of â€Å"it resembled† is implied.) 5. â€Å"Other cops have an alternative solution, they simply arrive on the scene long after the criminals have fled in order to avoid any confrontation.† Employ a colon in place of the comma when what follows is a definition or explanation stemming from the first clause: â€Å"Other cops have an alternative solution: They simply arrive on the scene long after the criminals have fled in order to avoid any confrontation.† Better yet, to create a stronger impact with the sentence, move the final modifying phrase forward as a parenthetical: â€Å"Other cops have an alternative solution: In order to avoid any confrontation, they simply arrive on the scene long after the criminals have fled.† More than one of these strategies is usually an option; each of the sentences above can be repaired with at least two of the methods described. Often, however, depending on the sentence content and structure, one solution will stand out as the best. (An em dash can also be used to set one independent clause off from the other.) Want to improve your English in five minutes a day? Get a subscription and start receiving our writing tips and exercises daily! Keep learning! Browse the Punctuation category, check our popular posts, or choose a related post below:30 Synonyms for â€Å"Meeting†Flier vs. FlyerPreposition Mistakes #3: Two Idioms

Problem and solution Essays

Problem and solution Essays Problem and solution Essay Problem and solution Essay Name: Instructor: Course: Date: Problem and solution Problem The education in USA is not up to the required standards. This is mainly experienced in elementary and high schools. These two levels are experiencing constraints in facilities and focusing on education seemed to be wrong. Almost all schools complain of too many children in one classroom. There are inadequate books and other basic facilities for learning. The private schools are also affected by this problem, but the government schools are worse than private ones. The teachers receive poor salaries and this demoralizes them from doing their work satisfactorily. Teaching students is a noble task, especially students in basic level. Teachers should be compensated adequately for them to be motivated in their work. Parents are complaining that teachers are not focused on the right purpose. They argue that teachers are confusing preparing students for the next level, with overworking them. For instance, students in high school should be prepared academically and socially to join higher learning. Instead, some teachers are giving them many assignments, which will not necessarily help them. Teachers need to be moderate on teaching and giving assignments to students. Teachers should focus more on quality teaching than quantity. Too much work for students does not necessarily mean they understand everything they do. Some teachers are not keen on how they assign work to the students. Some methods of assigning work to students are ineffective, but some teachers have not identified that. Solution The government needs to allocate more funds for education in basic levels. The funds should be used to improve the learning of environment for students. Enough books should be bought for all students. More classrooms should be built to accommodate the extra students in the present classrooms. All the classrooms should have the required facilities like desks for the students. The funds should also be used to remunerate teachers as required. Most teachers are receiving salaries below their grades. They deserve good remuneration according to their grade. The ministry of education needs to prepare programs, which will offer guidance to teachers on how to offer quality education to students. They may also be required to revise the education system if it is necessary. Observations have been made, and some conclusions state the education system does not cover all students need to learn. Parents need to support teachers in helping students to do excellently in their academic work. Some parents tend to be too busy to follow-up their children. Some just think it is the teacher’s duty to ensure students perform excellently. Students have a responsibility of cooperating with both teachers and parents. They need to address any issues inhibiting them from performing well in their studies. Students face many challenges, and they have a duty of seeking help, in case teachers and parents do not seem to realize their problems. They should assess on how the teachers are handling them as well as the rest of the learning environment. Students should know they are the beneficial of education and should suggest all possible ways of improving it. The most appropriate solution for this education problem is government intervention of increasing funds. It will be easy to solve financial problems, which are deteriorating the quality of education. Part of the extra funds could be used to fund the program for coaching educators on quality teaching modes. It would also be possible to revise the education system. Students need to follow an updated system, which is applicable in the current era. Education is a fundamental in development and growth. Therefore, the government needs to act swiftly.

APPLE INCORPORATED Research Paper Example | Topics and Well Written Essays - 1250 words

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Entrepreneurship Acunu Ltd Assignment Example | Topics and Well Written Essays - 1250 words

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