Lexical Effects in Perception of Tamil Geminates

Lexical status effects are a phenomenon in which listeners use their prior lexical knowledge of a language to identify ambiguous speech sounds in a word based on its word or nonword status. This phenomenon has been demonstrated for ambiguous initial English consonants (one example being the Ganong Effect, a phenomenon in which listeners perceive an ambiguous speech sound as a phoneme that would complete a real word rather than a nonsense word) as a supporting factor for top-down lexical processing affecting listeners' subsequent acoustic judgement, but not for ambiguous mid-word consonants in non-English languages. In this experiment, we attempt to look at ambiguous mid-word consonants with Tamil, a South Asian language in order to see if the same top-down lexical effect was applicable outside of English. These Tamil consonants can present as either singletons (single speech sounds) or geminates (doubled speech sounds).We hypothesized that by creating ambiguous stimuli between a geminate word kuppam and a singleton non-word like kubam, participants would be more likely to perceive the ambiguous sound as a phoneme that completes the real word rather than the nonword (in this case, perceiving the ambiguous sound as a /p/ for kuppam instead of kubam). Participants listened to the ambiguous stimuli in two separate sets of continua (kuppam/suppam and nakkam/pakkam) and then indicated which word they heard in a four-alternative forced choice word identification task. Results showed that participants identified the ambiguous sounds as the sound that completed the actual word, but only for one set of continua (kuppam/suppam). These data suggest that there may be strong top-down lexical effects for ambiguous sounds in certain stimuli in Tamil, but not others.No embargoAcademic Major: LinguisticsAcademic Major: Psycholog

The diachronic emergence of retroflex segments in three languages

The present study shows that though retroflex segments can be considered articulatorily marked, there are perceptual reasons why languages introduce this class into their phoneme inventory. This observation is illustrated with the diachronic developments of retroflexes in Norwegian (North- Germanic), Nyawaygi (Australian) and Minto-Nenana (Athapaskan). The developments in these three languages are modelled in a perceptually oriented phonological theory, since traditional articulatorily-based features cannot deal with such processes

The violability of backness in retroflex consonants

This paper addresses remarks made by Flemming (2003) to the effect that his analysis of the interaction between retroflexion and vowel backness is superior to that of Hamann (2003b). While Hamann maintained that retroflex articulations are always back, Flemming adduces phonological as well as phonetic evidence to prove that retroflex consonants can be non-back and even front (i.e. palatalised). The present paper, however, shows that the phonetic evidence fails under closer scrutiny. A closer consideration of the phonological evidence shows, by making a principled distinction between articulatory and perceptual drives, that a reanalysis of Flemming’s data in terms of unviolated retroflex backness is not only possible but also simpler with respect to the number of language-specific stipulations

Speech to text translation enabling multilingualism.

Speech acts as a barrier to communication between two individuals and helps them in expressing their feelings, thoughts, emotions, and ideologies among each other. The process of establishing a communicational interaction between the machine and mankind is known as Natural Language processing. Speech recognition aids in translating the spoken language into text. We have come up with a Speech Recognition model that converts the speech data given by the user as an input into the text format in his desired language. This model is developed by adding Multilingual features to the existent Google Speech Recognition model based on some of the natural language processing principles. The goal of this research is to build a speech recognition model that even facilitates an illiterate person to easily communicate with the computer system in his regional language

RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING

This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate

Bilingual phonology in dichotic perception: A case study of Malayalam and English voicing

Listeners often experience cocktail-party situations, encountering multiple ongoing conversa- tions while tracking just one. Capturing the words spoken under such conditions requires selec- tive attention and processing, which involves using phonetic details to discern phonological structure. How do bilinguals accomplish this in L1-L2 competition? We addressed that question using a dichotic listening task with fluent Malayalam-English bilinguals, in which they were pre- sented with synchronized nonce words, one in each language in separate ears, with competing onsets of a labial stop (Malayalam) and a labial fricative (English), both voiced or both voiceless. They were required to attend to the Malayalam or the English item, in separate blocks, and report the initial consonant they heard. We found that perceptual intrusions from the unattended to the attended language were influenced by voicing, with more intrusions on voiced than voiceless tri- als. This result supports our proposal for the feature specification of consonants in Malayalam- English bilinguals, which makes use of privative features, underspecification and the “standard approach” to laryngeal features, as against “laryngeal realism”. Given this representational account, we observe that intrusions result from phonetic properties in the unattended signal being assimilated to the closest matching phonological category in the attended language, and are more likely for segments with a greater number of phonological feature specifications