In the online world, especially in the social media platform most of us write without much regard to correct spelling and grammar. The spelling mistakes are much larger in proportion when it comes to Bangla language. In our paper, we presented a method for error detection and correction in Bangla words' spellings. Our system could detect a misspelled Bangla word and provide two following services-suggesting correct spellings for the word and correcting the word. We had used Norvig's algorithm for the purpose but instead of using probabilities of the words to prepare the suggestions and corrections, we had used Jaro-Winkler distance. The previous works done in this field for Bangla language are either very slow or offers less accuracy. Our system successfully achieved a 97% accuracy when evaluated with 1000 Bangla words.
This research was conducted to determine the awareness level of the students’ at the Universityof Malaya (UM) towards gender equality in achieving SDG5. A set of survey questionnaire established onKnowledge, Attitude, and Practices (KAP) was distributed through an online Google survey form to allUM students’, and 123 responses were collected to evaluate the awareness level (95% confidenceinterval with ± 5% margin of error). Data analysis was conducted through SPSS software. The resultsrevealed that the respondents have a higher knowledge level with lower attitude and practices levels.Spearman’s Rho coefficient correlation was used to evaluate the relationship level within variables(between knowledge and practices and attitude and practices). The results reported a weak positivecorrelation within knowledge and practice levels (r= .275, N= 123, p=
In this paper, the process of recognizing some important words from a large set of vocabularies is demonstrated based on the combination of dynamic and instantaneous features of the speech spectrum. There are many procedures to recognize a word by its vowel, but this paper presents the highly effective speaker independent speech recognition in a typical room environment noise cases. To distinguish several isolated words of the sound of different vowels, two important features such as Pitch and Formant are extracted from the speech signals collected from a number of random male and female speakers. The extracted features are then analyzed for the particular utterances to train the system. The specific objectives of this work are to implement an isolated and automatic word speech recognizer, which is capable of recognizing as well as responding to speech and an audio interfacing system between human and machine for an effective human-machine interaction. The whole system has been tested using computer codes, and the result was satisfactory in almost 90% of cases. However, the system might get confused by similar vowel sounds sometimes.
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