Search results
Results from the WOW.Com Content Network
Sri Suryaraya Andhra Nighantuvu is a Telugu language dictionary. It is the most comprehensive monolingual Telugu dictionary. [1] It was published in eight volumes between 1936 and 1974. [2] [3] It was named after Rao Venkata Kumara Mahipati Surya Rau, the zamindar of Pitapuram Estate who sponsored the first four volumes of the dictionary. [4] [5]
Used in Vemuri Rao's English-Telugu Dictionary (2002) Rice University's Reverse Transliteration System (RTS) (created by Ramarao Kanneganti and Ananda Kishore) can be used for the transliteration of Telugu into Roman script as an alternative to phonetic alphabet. [4]
Telugu script (Telugu: తెలుగు లిపి, romanized: Telugu lipi), an abugida from the Brahmic family of scripts, is used to write the Telugu language, a Dravidian language spoken in the Indian states of Andhra Pradesh and Telangana as well as several other neighbouring states.
A symbiotic relationship between the early Telugu blogger-Wikipedians with the larger Telugu online community was a big boon for the Telugu Wikipedia. Its initial momentum was furthered by committed members such as Kasubabu, Dr Rajasekhar, Chandrakantha Rao, Ravichandra, Veeven, Kasyap, Rahamtulla and Sujatha.
Telugu script is an abugida comprising 60 symbols – 16 vowels, 3 vowel modifiers, and 41 consonants. Telugu has a complete set of letters that follow a system to express sounds. The script is derived from the Brahmi script like those of many other Indian languages.
The Earth) is a 1980 Telugu-language philosophical long poem by C. Narayana Reddy. [1] It is written in free verse and was an outcome of Narayana Reddy's meditation on the meaning and mystery of human existence. [2] It deals with the theme of universal brotherhood and the quest of man for the meaning of life and of the nature of the universe ...
Telugu Alankaram is a figure of speech which means ornaments or embellishments which are used to enhance the beauty of the poems. There are two types of Alankarams, 'Shabdalankaram' which primarily focuses on Sound and 'Arthalamkaram' which focuses on meaning. These two alankarams are further broken down in to different categories.
In audio part we use features like log mel spectrogram, mfcc etc. from the raw audio samples and we build a model to get feature vector out of it . For visual part generally we use some variant of convolutional neural network to compress the image to a feature vector after that we concatenate these two vectors (audio and visual ) and try to ...