American Literature The Romantic Era (Part 1) Flashcard

American Literature The Romantic Era (Part 1)

Teenagers and Depression essays

Teenagers and Depression essays Teenagers experience a great deal of life changing events and some studies even suggest that the teenage years may be the most stressful time in people lives. Academics are the most effective in teenage depression; putting in effort to make exceptional grades, pass exams and having enough credits to graduate. Another cause of teenage depression is sleep deprivation, which consist of unhealthy sleeping habits due to studying late and fitting in a healthy social life. Teenagers also have worries about money as far as how they will pay for school events and keeping up with fashion trends to fit in to society are the most popular ways of money worries. Then there are relationship concerns with family, friends, and boyfriends or girlfriends. You have to maintain a healthy relationship and find solutions to relationship problems. Finally, the uncertainty of their future is a big worry and deals with a teenagers mental health. If these issues and worries are not balanced it causes depressio n and as a great effect on teenagers. The result of academic stress, sleep deprivation, money worries, and uncertainty about their future is depression that turns a teen to the use of tobacco, alcohol, other drugs, and unhealthy eating habits. Teens feel if they use these methods it will clear their minds and create a stress free life but it doesnt. Teens already have money worries and buying addictive drugs create more which drives a teenager to depression. The substances may make them feel as if the problems have cease but soon after the chemicals that relax the brain die off they begin to think about their problems. Drugs are not a solution but have many reasons why teens support their habits of doing it. The use of tobacco is common with teens in order to relieve their stress and take away the thoughts of not doing well in school or not having enough money to fit in to society popularity. Tobaccos consist of chemicals that can...

Answers to West Point Admission Questions Essay

Answers to West Point Admission Questions - Essay Example In addition to inspiration from the Army, I also draw inspiration from my country. This is a great nation and the whole world looks up to it. I’m a proud citizen of USA and I wish to express my gratitude to this great country by serving its citizens and protecting its land. I believe that the Army has multiple tasks. They are not just the defenders of a nation; soldiers are servants of the people of that nation. American soldiers are warriors and defenders; they are there to serve the people of USA. West Point’s Military Academy is a renowned name in preparing graduates who have an extreme sense of duty and honor for their country. In addition, the graduates are also well disciplined. I have always been inspired by discipline of cadets. I believe that’s the way life should be: organized and well planned; for discipline makes man superior to other creatures. Personally I’m a disciplined person; I like to organize my life whenever I can. I believe that disci pline is the key to achievement. If one goes well planned and disciplined, there’s nothing that cannot be achieved. In addition, I am persistent by nature. When I start something, I rarely give it up or quit on it and I like challenging tasks like problem solving in limited time. Once I do get the chance to attend US military academy, I’d definitely want to work as an active duty army officer. I’d prefer active duty since I want to devote my whole life to US Army, I long to be a part of it. I’m also inspired by the military life. Reporting daily, being posted at a base camp; where I can learn about Army life and its challenges. Going to the USMA will also be good for my health. Undergoing physical training and extensive exercise daily will make me physically strong and daily drills will develop more discipline within me.I believe that discipline is the basic and foremost quality needed to become a successful USMA cadet. At USMA, the academy and its dignified staff strive to groom young pupils into responsible and mature cadets; for this process to be accomplished, I believe the pupils required to display best discipline. Disciplinary education is unique to military academies; USMA is no exception. Discipline is actually the component of a cadet’s life that I appreciate the most.

West Africa the Atlantic Slave-Trade Essay Example | Topics and Well Written Essays - 750 words

West Africa the Atlantic Slave-Trade - Essay Example As an outcome Africans were taken to North America, Central America, South America and Caribbean to offer slave labor in gold and silver mines and agricultural plantations growing crops such as cotton sugar and tobacco (Rodney 125). The tarnished commerce of the human being persisted for more than 400 years since the Atlantic slave trade did come to an end in the late 1870’s. Atlantic slave trade was systematized in Europe and about the huge profits made by countries such as England and France. Africans journey from Africa to America across Atlantic Ocean was a terrible one. Africans were crowded like sardines on the slave ships of the Atlantic full of oppression and brutality which they replied nobly (Rodney 125). The precise number of how many Africans were taken from their families to be sold as slaves is not known, but it is estimated that 15 million slaves reached the American continent and the Caribbean island because of Atlantic slave trade. The number of slaves who left Africa soil was much higher than 15 million since some were killed during the brutal process of acquiring the slaves and also some died on board. By the 19th century, there was a modification as people who took the leading role in ill-using Africa. The European Countries themselves were inactive in the slave trade; in its place European who had established themselves in Brazil, North America and Cuba were the ones who planned the trade. America had gained independence from the Britain and it was the new nation of United States of America which played the greatest role in the last 50 years of the Atlantic slave trade, by taking back slaves at a greater value than ever before (Rodney 126). In order to be in a commerce relationship with West Africa, most of the European countries decided to up factories on the coast. A factory in the trading language of the West African coast was a place where European and African products

Describe the three major causes of soil erosion Essay

Describe the three major causes of soil erosion - Essay Example The formation of a 1 centimeter soil can take up to 400 years and the production of a sufficient depth of farming might take 3,000-12,000 years (Edwards, 2005, p. 36). Soils are easily and increasingly eroded but may take years to form, leading to ruining of land resources. Soil erosion rapidly occurs in mismanaged lands, lands where protective vegetation is removed, places with rapid population growth, steep lands, and places with extreme climatic conditions or rainfall is seasonal, downpour, and unreliable (Edwards, 2005, p. 36). In line with the rapid occurrence of soil erosion are major causes of soil erosion which the essay will discuss in detail. This includes overcultivation, overgrazing, and deforestation. Soil erosion is a natural process but most of the human interventions contribute to the increased incidences of soil erosion. The potential harm to the ecological balance, biological species, and human harm is insurmountable; thus, this paper will discuss in addition the ca use, promoting factors, effect, and examples of overcultivation, overgrazing, and deforestation observed within the society. ... In addition, overcultivation partly occurs due to introduction and use of mechanized machinery such as tractors and discs ploughs and the introduction of irrigation schemes (Park, 2001, p. 438). Overcultivation is one of the major causes of soil erosion. The constant use of land for crop production removes the protective soil covering and crops do not have the capacity to strongly hold the soil, which increase the risks of soil erosion. Likewise, the use of tractors and disc ploughs destroy native perennial vegetation, encourage soil degradation, and remove protective soil cover (Park, 2001, p. 438). Due to the removal of soil covering, topsoil is exposed to wind erosion and blown away, making the soil dry and infertile. Water irrigation is the proposed solution for dry lands but often increases soil salinity and water logging which may also increase the likelihood of erosion if left abandoned (Park, 2001, p. 438). Thus, it can be inferred that when there is overcultivation, the poss ibility of soil erosion is likewise to occur. Overgrazing Overgrazing is the most widespread cause of soil erosion and occurs when there are too many animals for the amount of grass available (Edwards, 2005, p. 36; Waugh, 2003, p. 254). Overgrazing is common among traditional farmers who rely heavily on grazing animals. Overgrazing can be attributed to a variety of factors such as status symbol, food security, food supply, rise of export agriculture, and veterinary care (Park, 2001, p. 438). Overgrazing makes the soil condition worse. Palatable plants are replaced by unpalatable plants, pressure increases on the less-grazed pasture, bare ground, sand sheets and dunes increases which

Speaker Independent Speech Recognizer Development

Speaker Independent Speech Recognizer Development Chapter 4 Methodology and Implementation This chapter describes the methodology and implementation of the speaker independent speech recognizer for the Sinhala language and the Android mobile application for voice dialing. Mainly there are two phases of the research. First one is to build the speaker independent Sinhala speech recognizer to recognize the digits spoken in Sinhala language. The second phase is to build an android application by integrating the trained speech recognizer. This chapter covers the tools, algorithms, theoretical aspects, the models and the file structures used for the entire research process. 4.1Research phase 1: Build the speaker independent Sinhala speech recognizer for recognizing the digits. In this section the development of the speaker independent Sinhala speech recognizer is described, step by step. It includes the phonetic dictionary, language model, grammar file, acoustic speech database and the trained acoustic model creation. 4.1.1 Data preparation This system is a Sinhala speech recognition voice dial and since there is no such speech database which is done earlier was available, the speech has to be taken from the scratch to develop the system. Data collection The first stage of any speech recognizer is the collection of sound signals. Database should contain a variety of enough speakers recording. The size of the database is compared to the task we handle. For this application only little number of words was considered. This research aims only the written Sinhala vocabulary that can be applied for voice dialing. Altogether twelve words were considered with the ten numbers including two initial calling words â€Å"amatanna† and â€Å"katakaranna†. Here the Database has two parts, the training part and the testing part. Usually about 1/10th of the full speech data is used to the testing part. In this research 3000 speech samples were used for training and 150 speech samples were used for testing. Speech database Before collecting data, a speech database was created. The database was included with the Sinhala speech samples taken from variety of people who were in different age levels. Since there was no such database published anywhere for Sinhala language relevant for voice dialing, speech had to be collected from Sinhala native speakers. Prompt sheet To create the speech database, the first step was to prepare the prompt sheet having a list of sentences for all the recordings. Here it used 100 sentences that are different from each other by generating the numbers randomly. 50 sentences are starting with the word â€Å"amatanna† while the other half is starting with the word â€Å"katakaranna†. The prompt sheet used for this research is given in the Appendix A. Recording The prepared sentences in the prompt sheet were recorded by using thirty (30) native speakers since this is speaker independent application. The speakers were selected according to the age limits and divided them into eight age groups. Four people were selected from each group except one age group. Two females and two males were included into each age group. One group only contained two people with one female and one male. Each speaker was given 100 sentences to speak and altogether 3000 speech samples were recorded for training. The description of speakers such as gender and age can be found in Appendix A. If there was an error in the recording due to the background noise and filler sounds, the speaker was asked to repeat it and got the correct sound signal. Since the proposed system is a discrete system, the speakers have to make a short pause at the start and end of the recording and also between the words when they were uttered. Speech was recorded in a quiet room and the recordi ngs were done at nights by using a condenser recorder microphone. The sounds were recorded under the sampling rate of 44.1 kHz using mono channel and they were saved under *.wav format. Sampling frequency and format of speech audio files Speech recording files were saved in the file format of MS WAV. The â€Å"Praatâ€Å" software was used to convert the 44.1 kHz sampling frequency signals to 16 kHz frequency signals since the frequency should be 16kHz of the training samples. Audio files were recorded in a medium length of 11 seconds. Since there should be a silence in the beginning and the end of the utterance and it should not be exceeded 0.2 seconds, the â€Å"Praat† software was used to edit all 3000 sound signals. 4.1.2 Pronunciation dictionary The pronunciation dictionary was implemented by hand since the number of words used for the voice dialing system is very few. It is used only 12 words from the Sinhala vocabulary. To create the dictionary, the International Phonetic Alphabet for Sinhala Language and the previously created dictionaries by CMU Sphinx were used. But the acoustic phones were taken mostly by studying the different types of databases given by the Carnegie Mellon University’s Sphinx Forum (CMU Sphinx Forum). Two dictionaries were implemented for this system. One is for the speech utterances and the other one is for filler sounds. The filler sounds contain the silences in the beginning, middle and at the end of the speech utterances. The attachment of the two types of dictionaries can be found on the Appendix A. They are referred to as the languagedictionaryand thefiller dictionary. 4.1.3 Creating the grammar file The grammar file also created by hand since the number of words used for the system is very few. The JSGF (JSpeech Grammar Format) format was used to implement the grammar file. The grammar file can be found in Appendix A. 4.1.4 Building the language model Word search is restricted by a language model. It identifies the matching words by comparing the previously recognized words by the model and restricts the matching process by taking off the words that are not possible to be. N-gram language model is the most common language models used nowadays. It is a finite state language model and it contains statistics of word sequences. In search space where restriction is applied, a good accuracy rate can be obtained if the language model is a very successful one. The result is the language model can predict the next word properly. It usually restricts the word search which are included the vocabulary. The language model was built using the cmuclmtk software. First of all the reference text was created and that text (svd.text) can be found in Appendix A. It was written in a specific format. The speech sentences were delimited byandtags. Then the vocabulary file was generated by giving the following command. text2wfreq svd.vocab Then the generated vocabulary file was edited to remove words (numbers and misspellings). When finding misspellings, they were fixed in the input reference text. The generated vocabulary file (svd.vocab) can be found in the Appendix A. Then the ARPA format language model was generated using these commands. text2idngram -vocab svd.vocab -idngram svd.idngram idngram2lm -vocab_type 0 -idngram svd.idngram -vocab svd.vocab –arpa svd.arpa Finally the CMU binary of language model (DMP file) was generated using the command sphinx_lm_convert -i svd.arpa -o svd.lm.DMP The final output containing the language model needed for the training process is svd.lm.dmp file. This is a binary file. 4.1.5Acoustic model Before starting the acoustic model creation, the following file structure was arranged as described by the CMU Sphinx tool kit guide. The name of the speech database is â€Å"svd† (Sinhala Voice Dial). The content of these files is given in Appendix A. svd.dic -Phonetic dictionary svd.phone -Phoneset file svd.lm.DMP -Language model svd.filler -List of fillers svd _train.fileids -List of files for training svd _train.transcription -Transcription for training svd _test.fileids -List of files for testing svd _test.transcription -Transcription for testing All these files were included in to one directory and it was named as â€Å"etc†. The speech samples of wav files were included in to another directory and named it as â€Å"wav†. These two directories were included in to another directory and named it using the name of the database (svd). Before starting the training process, there should be another directory that contains the â€Å"svd† and the required compilation package â€Å"pocketsphinx†, â€Å"sphinxbase† and â€Å"sphinxtrain† directories. All the packages and the â€Å"svd† directory were put into another directory and started the training process. Setting up the training scripts The command prompt terminal is used to run the scripts of the training process. Before starting the process, terminal was changed to the database â€Å"svd† directory and then the following command was run. python ../sphinxtrain/scripts/sphinxtrain –t svd setup This command copied all the required configuration files into etc sub directory of the database directory and prepared the database for training. The two configuration files created were feat.params and sphinx_train.cfg. These two are given in Appendix A. Set up the database These values were filled in at configuration time. The Experiment name, will be used to name model files and log files in the database. $CFG_DB_NAME = svd; $CFG_EXPTNAME = $CFG_DB_NAME; Set up the format of database audio Since the database contains speech utterances with the ‘wav’ format and they were recorded using MSWav, the extension and the type were given accordingly as â€Å"wav† and â€Å"mswav†. $CFG_WAVFILES_DIR = $CFG_BASE_DIR/wav; $CFG_WAVFILE_EXTENSION = wav; $CFG_WAVFILE_TYPE = mswav; # one of nist, mswav, raw Configure Path to files This process was done automatically when having the right file structure in the running directory. The naming of the files must be very accurate. The paths were assigned to the variables used in main training of models. $CFG_DICTIONARY = $CFG_LIST_DIR/$CFG_DB_NAME.dic; $CFG_RAWPHONEFILE = $CFG_LIST_DIR/$CFG_DB_NAME.phone; $CFG_FILLERDICT = $CFG_LIST_DIR/$CFG_DB_NAME.filler; $CFG_LISTOFFILES = $CFG_LIST_DIR/${CFG_DB_NAME}_train.fileids; $CFG_TRANSCRIPTFILE = $CFG_LIST_DIR/${CFG_DB_NAME}_train.transcription; $CFG_FEATPARAMS = $CFG_LIST_DIR/feat.params; Configure model type and model parameters The model type continuous and semi continuous can be used in pocket sphinx. Continuous type is used for continuous speech recognition. Semi continuous is used for discrete speech recognition process. Since this application use discrete speech the semi continuous model training was used. #$CFG_HMM_TYPE = .cont.; # Sphinx 4, Pocketsphinx $CFG_HMM_TYPE = .semi.; # PocketSphinx $CFG_FINAL_NUM_DENSITIES = 8; # Number of tied states (senones) to create in decision-tree clustering $CFG_N_TIED_STATES = 1000; The number of senones used to train the model is indicated in this value. The sound can be chosen accurately if the number of senones is higher. But if we use too much senones, then it may not be able to recognize the unseen sounds. So the Word Error Rate can be very much higher on unseen sounds. The approximate number of senones and number of densities is provided in the table below. Configure sound feature parameters The default parameter used for sound files in Sphinx is a rate of 16 thousand samples per second (16KHz). If this is the case, then the etc/feat.params file will be automatically generated with the recommended values. The Recommended values are: # Feature extraction parameters $CFG_WAVFILE_SRATE = 16000.0; $CFG_NUM_FILT = 40; # For wideband speech its 40, for telephone 8khz reasonable value is 31 $CFG_LO_FILT = 133.3334; # For telephone 8kHz speech value is 200 $CFG_HI_FILT = 6855.4976; # For telephone 8kHz speech value is 3500 Configure decoding parameters The following were properly configured in theetc/sphinx_train.cfg. $DEC_CFG_DICTIONARY = $DEC_CFG_BASE_DIR/etc/$DEC_CFG_DB_NAME.dic; $DEC_CFG_FILLERDICT = $DEC_CFG_BASE_DIR/etc/$DEC_CFG_DB_NAME.filler; $DEC_CFG_LISTOFFILES = $DEC_CFG_BASE_DIR/etc/${DEC_CFG_DB_NAME}_test.fileids; $DEC_CFG_TRANSCRIPTFILE = $DEC_CFG_BASE_DIR/etc/${DEC_CFG_DB_NAME}_test.transcription; $DEC_CFG_RESULT_DIR = $DEC_CFG_BASE_DIR/result; # These variables, used by the decoder, have to be user defined, and # may affect the decoder output $DEC_CFG_LANGUAGEMODEL_DIR = $DEC_CFG_BASE_DIR/etc; $DEC_CFG_LANGUAGEMODEL = $DEC_CFG_LANGUAGEMODEL_DIR/ ${CFG_DB_NAME}.lm.DMP; Training After setting all these paths and parameters in the configuration file as described above, the training was proceeded. To start the training process the following command was run. python ../sphinxtrain/scripts/sphinxtrain run Scripts launched jobs on the machine, and it took few minutes to run. Acoustic Model After the training process, the acoustic model was located in the following path in the directory. Only this folder is needed for the speech recognition tasks. model_parameters/svd.cd_semi_200 We need only that folder for the speech recognition tasks we have to perform. 4.1.6Testing Results 150 speech samples were used as testing data. The aligning results could be obtained after the training process. It was located in the following path in the database directory. results/svd.align 4.1.7Parameters to be optimized Word error rate WER was given as a percentage value. It was calculated according to the following equation Accuracy Accuracy was also given as a percentage. That is the opposite value of the WER. It was calculated using the following equation To obtain an optimal recognition system, the WER should be minimized and the accuracy should be maximized. The parameters of the configuration file were changed time to time and obtained an optimal recognition system where the WER was the minimum with a high accuracy rate. 4.2Research phase 2: Build the voice dialing mobile application. In this section, the implementation of voice dialer for android mobile application is described. The application was developed using the programming language JAVA and it was done using the Eclipse IDE. It was tested in both the emulator and the actual device. The application is able to recognize the spoken digits by any speaker and dial the recognized number. To do this process the trained acoustic model, the pronunciation dictionary, the language model and the grammar files were needed. The speech recognition was performed by using these models in the mobile device itself by using the pocketsphinx library. It is a library written in C language to use for embedded speech recognition devices in Android platform. The step by step implementation and integration of the necessary components were discussed in detail in this section. Resource Files When inputting the resource files to the Android application, they were added in to theassets/directory of the project. Then the physical path was given to make them available for pocketsphinx. After adding them, the Assets directory contained the following resource files. Dictionary svd.dic svd.dic.md5 Grammar digits.gram digits.gram.md5 menu.gram menu.gram.md5 Language model svd.lm.DMP svd.lm.DMP.md5 Acoustic Model feat.params feat.params.md5 mdef mdef.md5 means means.md5 mixture_weights mixture_weights.md5 noisedict noisedict.md5 transition_matrices transition_matrices.md5 variances variances.md5 Assets.lst models/dict/svd.dic models/grammar/digits.gram models/grammar/menu.gram models/hmm/en-us-semi/feat.params models/hmm/en-us-semi/mdef models/hmm/en-us-semi/means models/hmm/en-us-semi/mixture_weights models/hmm/en-us-semi/noisedict models/hmm/en-us-semi/sendump models/hmm/en-us-semi/transition_matrices models/hmm/en-us-semi/variances models/lm/svd.lm.DMP Setup the Recognizer First of all the recognizer should be set up by adding the resource files. The model parameters taken after the training process were added as the HMM in the application. The recognition process was depended mainly on this resource files. Since the grammar files and the language model were added as assets, these two can be used for the recognition process of the application as well as the HMM. The utterances can be recognized from either the grammar files or language model. The whole process is coded using the Java programing language. 4.3Architecture of the developed Speech Recognition System

The Crysanthemums Essays -- Literary Analysis, John Steinbeck

John Steinbeck uses his unique literary style to write the short story â€Å"The Chrysanthemums,† where he brings his readers to a society of inequality amongst the genders. â€Å"The Chrysanthemums† depicts the challenges of Elisa Allen, a thirty five-year-old woman who is expected to be a traditional housewife. Her ongoing transformation throughout the story portrays the life of a woman trying to gain meaning in her dull life during the 1930’s. John Steinbeck's, â€Å"The Chrysanthemums,† shows the true feelings of the protagonist, Elisa Allen, through the use of femininity, self-awareness, and weakness. Elisa’s character undergoes a complete transformation of femininity, due to her conversation with the tinker. The story initially describes Elisa’s appearance using words associated with manliness, as Steinbeck states, her face is â€Å"strong, eager, and handsome,† and her figure is â€Å"blocked and heavy† (228). Furthermore, she wears a man's hat, heavy leather gloves, and a big apron that hides her printed dress (228). As a result, she is depicted as a woman with greater masculine qualities than feminine qualities. However, as soon as she encounters the tinker and notices his interest in Chrysanthemums, â€Å"the irritation melted from Elisa’s face† (232), and eventually reveals her womanly side. After the tinker left, she â€Å"scrubbed herself with a little block of pumice, legs and thighs, loins and chest and arms, until her skin was scratched and red† (236). She then bathes and puts on a dress to make herself look mor e feminine (237). For the first time, Elisa feels valued and special by the tinker. As a result, she puts more effort into beautifying herself than the house or garden. Therefore, one can see that although Elisa is i... ...ouse wives, and mothers who are fragile and insignificant. Instead, she is to remain in a â€Å"closed pot† (228), just as she is expected to do. As a result, she cries at the truth that she will always be reminded, that she is a â€Å"weak† and â€Å"useless† woman, which only increases her frustrations and dissatisfactions about her marriage (238). In conclusion, Steinbeck’s â€Å"The Chrysanthemums† illustrates the life of Elisa Allen, who struggles with womanhood, self-recognition, and impotence. Although, she is described as a modern house wife of the 1930's, it is clear, that she is far from the average traditional spouse. Rather, she yearns to be represented in the masculine world. However, through Elisa’s tribulations and limitations, she has unfortunately lessoned her stature. Therefore, one should learn to make the best use of our present rights of equality.