Relevant bibliographies by topics / Tamil speech recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Tamil speech recognition'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Tamil speech recognition"

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Rojathai, S., and M. Venkatesulu. "Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition." International Journal of Software Innovation 2, no. 2 (April 2014): 43–53. http://dx.doi.org/10.4018/ijsi.2014040103.

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In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.
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Rojathai, S., and M. Venkatesulu. "Tamil Speech Word Recognition System with Aid of ANFIS and Dynamic Time Warping (DTW)." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6719–27. http://dx.doi.org/10.1166/jctn.2016.5619.

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It is unfortunate that though the extant Tamil speech word recognition techniques have come out successful in detecting speech words from the speech word database by means of MFCC (Mel Frequency Cepstral Coefficients) features and FFBNN (Feed Forward Back Propagation Neural Network), they seem to have failed miserably to come up to expectations by generating less than desired outcomes of recital in terms of precision. Hence we have proudly launched, through this document, an innovative Tamil speech recognition technique to address the challenge by making use of novel features with ANFIS (Adaptive Neuro Fuzzy Inference System) based recognition method. Thus, at the outset, preprocessing is performed to cut down the noise in the input speech signals. Thereafter, feature vectors are mined from the preprocessed speech signals and furnished to the ANFIS. The epoch making technique is home to three novel features such as Energy Entropy, Short Time Energy and Zero Crossing Rate which are mined from the Tamil speech word signals and subjected to the word recognition procedure, in which, the ANFIS system is well guided by the features from feature mining task and the recognition efficiency is authenticated by using a set of test speech words. In the course of the testing stage, with a view to achieving exact outcomes, the dynamic time warping is estimated by means of the guidance and test word feature values. The performance outcomes illustrate the fact that the innovative Tamil speech word recognition technique has been able to achieve amazing efficiency in recognizing the input Tamil speech words, in addition to yielding higher levels of achievement in terms of precision. Moreover, the accomplishment of the well-conceived recognition technique is assessed and contrasted with the modern Tamil speech word recognition techniques.
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Nelapati, Ratna Kanth, and Saraswathi Selvarajan. "Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (December 31, 2022): 166–73. http://dx.doi.org/10.17762/ijritcc.v10i2s.5924.

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The problem of inferring human emotional state automatically from speech has become one of the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used in several worksfor emotion recognition from speech, the potential of using probabilistic SVMs for this task is not explored. The emphasis of the current work is on how to use probabilistic SVMs for the efficient recognition of emotions from speech. Emotional speech corpuses for two Dravidian languages- Telugu & Tamil- were constructed for assessing the recognition accuracy of Probabilistic SVMs. Recognition accuracy of the proposed model is analyzed using both Telugu and Tamil emotional speech corpuses and compared with three of the existing works. Experimental results indicated that the proposed model is significantly better compared with the existing methods.
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Hashim Changrampadi, Mohamed, A. Shahina, M. Badri Narayanan, and A. Nayeemulla Khan. "End-to-End Speech Recognition of Tamil Language." Intelligent Automation & Soft Computing 32, no. 2 (2022): 1309–23. http://dx.doi.org/10.32604/iasc.2022.022021.

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Thangarajan, R., A. M. Natarajan, and M. Selvam. "Syllable modeling in continuous speech recognition for Tamil language." International Journal of Speech Technology 12, no. 1 (March 2009): 47–57. http://dx.doi.org/10.1007/s10772-009-9058-0.

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Suriya, Dr S., S. Nivetha, P. Pavithran, Ajay Venkat S., Sashwath K. G., and Elakkiya G. "Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms." EAI Endorsed Transactions on e-Learning 8, no. 2 (February 8, 2023): e1. http://dx.doi.org/10.4108/eetel.v8i2.3025.

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Computational linguistics is the branch of linguistics in which the techniques of computer science are applied to the analysis and synthesis of language and speech. The main goals of computational linguistics include: Text-to- speech conversion, Speech-to-text conversion and Translating from one language to another. A part of Computational Linguistics is the Character recognition. Character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. Character recognition methodology mainly focuses on recognizing the characters irrespective of the difficulties that arises due to the variations in writing style. The aim of this project is to perform character recognition for of one of the complex structures of south Indian language ‘Tamil’ using a supervised algorithm that increases the accuracy of recognition. The novelty of this system is that it recognizes the characters of the Predominant Tamil Language. The proposed approach is capable of recognizing text where the traditional character recognition systems fails, notably in the presence of blur, low contrast, low resolution, high image noise, and other distortions. This system uses Convolutional Neural Network Algorithm that are able to exact the local features more accurately as they restrict the receptive fields of the hidden layers to be local. Convolutional Neural Networks are a great kind of multi-layer neural networks that uses back-propagation algorithm. Convolutional Neural Networks are used to recognize visual patterns directly from pixel images with minimal preprocessing. This trained network is used for recognition and classification. The results show that the proposed system yields good recognition rates.
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Sarkar, Swagata, Sanjana R, Rajalakshmi S, and Harini T J. "Simulation and detection of tamil speech accent using modified mel frequency cepstral coefficient algorithm." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 426. http://dx.doi.org/10.14419/ijet.v7i2.33.14202.

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Automatic Speech reconstruction system is a topic of interest of many researchers. Since many online courses are come into the picture, so recent researchers are concentrating on speech accent recognition. Many works have been done in this field. In this paper speech accent recognition of Tamil speech from different zones of Tamilnadu is addressed. Hidden Markov Model (HMM) and Viterbi algorithms are very popularly used algorithms. Researchers have worked with Mel Frequency Cepstral Coefficients (MFCC) to identify speech as well as speech accent. In this paper speech accent features are identified by modified MFCC algorithm. The classification of features is done by back propagation algorithm.
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Geetha, K., and R. Vadivel. "Phoneme Segmentation of Tamil Speech Signals Using Spectral Transition Measure." Oriental journal of computer science and technology 10, no. 1 (March 4, 2017): 114–19. http://dx.doi.org/10.13005/ojcst/10.01.15.

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Process of identifying the end points of the acoustic units of the speech signal is called speech segmentation. Speech recognition systems can be designed using sub-word unit like phoneme. A Phoneme is the smallest unit of the language. It is context dependent and tedious to find the boundary. Automated phoneme segmentation is carried in researches using Short term Energy, Convex hull, Formant, Spectral Transition Measure(STM), Group Delay Functions, Bayesian Information Criterion, etc. In this research work, STM is used to find the phoneme boundary of Tamil speech utterances. Tamil spoken word dataset was prepared with 30 words uttered by 4 native speakers with a high quality microphone. The performance of the segmentation is analysed and results are presented.
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A, Akila, and Chandra E. "WORD BASED TAMIL SPEECH RECOGNITION USING TEMPORAL FEATURE BASED SEGMENTATION." ICTACT Journal on Image and Video Processing 5, no. 4 (May 1, 2015): 1037–43. http://dx.doi.org/10.21917/ijivp.2015.0152.

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Kalamani, M., M. Krishnamoorthi, and R. S. Valarmathi. "Continuous Tamil Speech Recognition technique under non stationary noisy environments." International Journal of Speech Technology 22, no. 1 (November 30, 2018): 47–58. http://dx.doi.org/10.1007/s10772-018-09580-8.

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Dissertations / Theses on the topic "Tamil speech recognition"

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Lin, Wei-Ting, and 林威廷. "A Design of Trilingual Speech Recognition System for Chinese, Turkish and Tamil." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/83040195001830259860.

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碩士
國立中山大學
電機工程學系研究所
100
In this thesis, both Turkish and Tamil, a language spoken in southern India and Sri Lanka, are studied in addition to Mandarin Chinese. It is hoped that the history, culture, and economy behind each language can be acquainted, tasted and appreciated during the learning process. In the ancient Chinese Han and Tang Dynasties, the “Silk Road” played the most magnificent role to connect among the Oriental China, the Western Turkey and the Southern India as the international trading corridor. In this modern era, Turkey and India are both the most important cotton exporting countries. Moreover, China, Turkey and India have been showing their potential to the newly emerging markets in the world. Therefore, a trilingual speech recognition system is developed and implemented to help us to learn Chinese, Turkish and Tamil, as well as to enhance our understanding to their history and culture. In this trilingual system, linear predicted cepstral coefficients, Mel-frequency cepstral coefficients, hidden Markov model and phonotactics are used as the two syllable feature models and the recognition model respectively. For the Chinese system, a 2,699 two-syllable words database is used as the training corpus. For the Turkish and Tamil systems, a database of 10 utterances per mono-syllable is established by applying their pronunciation rules. These 10 utterances are collected through reading 5 rounds of the same mono-syllables twice with tone 1 and tone 4. The correct rates of 88.30%, 84.21%, and 88.74% can be reached for the 82,000 Chinese, 30,795 Turkish, and 3,500 Tamil phrase databases respectively. The computation time for each system is within 1.5 seconds. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98% correct language-phrase recognition rate can be reached with the computation time less than 2 seconds.
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Book chapters on the topic "Tamil speech recognition"

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Sowmya, V., and A. Rajeswari. "Speech Emotion Recognition for Tamil Language Speakers." In Machine Intelligence and Signal Processing, 125–36. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1366-4_10.

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Saraswathi, S., and T. V. Geetha. "Building Language Models for Tamil Speech Recognition System." In Lecture Notes in Computer Science, 161–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30176-9_21.

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Saraswathi, S., and T. V. Geetha. "Implementation of Tamil Speech Recognition System Using Neural Networks." In Lecture Notes in Computer Science, 169–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30176-9_22.

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Srikanth, M., D. Pravena, and D. Govind. "Tamil Speech Emotion Recognition Using Deep Belief Network(DBN)." In Advances in Intelligent Systems and Computing, 328–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67934-1_29.

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Girirajan, S., and A. Pandian. "Convolutional Neural Network Based Automatic Speech Recognition for Tamil Language." In Lecture Notes in Electrical Engineering, 91–103. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4831-2_8.

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Betina, Antony J., Paul N. R. Rejin, and G. S. Mahalakshmi. "Applying entity recognition and verb role labelling for information extraction of Tamil biomedicine." In Artificial Intelligence and Speech Technology, 211–20. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003150664-24.

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Karpagavalli, S., R. Deepika, P. Kokila, K. Usha Rani, and E. Chandra. "Isolated Tamil Digit Speech Recognition Using Template-Based and HMM-Based Approaches." In Communications in Computer and Information Science, 441–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29216-3_48.

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Prayla Shyry, S., A. Christy, and Y. Bevish Jinila. "Speech Emotion Recognition of Tamil Language: An Implementation with Linear and Nonlinear Feature." In Lecture Notes in Electrical Engineering, 145–54. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9154-6_15.

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Akanksha, Akanksha. "Tamil Language Automatic Speech Recognition Based on Integrated Feature Extraction and Hybrid Deep Learning Model." In Lecture Notes in Networks and Systems, 283–92. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9719-8_23.

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Vimala, C., and V. Radha. "Efficient Speaker Independent Isolated Speech Recognition for Tamil Language Using Wavelet Denoising and Hidden Markov Model." In Lecture Notes in Electrical Engineering, 557–69. India: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1000-9_52.

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Conference papers on the topic "Tamil speech recognition"

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R., Kiran, Nivedha K., Pavithra Devi S., and Subha T. "Voice and speech recognition in Tamil language." In 2017 2nd International Conference on Computing and Communications Technologies (ICCCT). IEEE, 2017. http://dx.doi.org/10.1109/iccct2.2017.7972293.

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Akhilesh, A., Brinda P, Keerthana S, Deepa Gupta, and Susmitha Vekkot. "Tamil Speech Recognition Using XLSR Wav2Vec2.0 & CTC Algorithm." In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2022. http://dx.doi.org/10.1109/icccnt54827.2022.9984422.

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Harish, S., P. Vijayalakshmi, and T. Nagarajan. "Significance of segmentation in phoneme based Tamil speech recognition system." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941739.

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Ganesh, Akila A., and Chandra Ravichandran. "Grapheme Gaussian model and prosodic syllable based Tamil speech recognition system." In 2013 International Conference on Signal Processing and Communication (ICSC). IEEE, 2013. http://dx.doi.org/10.1109/icspcom.2013.6719821.

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Saraswathi, S., and T. V. Geetha. "Two Level Language Models for Improving the Performance of Tamil Speech Recognition." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.28.

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Karpagavalli, S., and E. Chandra. "Phoneme and word based model for tamil speech recognition using GMM-HMM." In 2015 International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2015. http://dx.doi.org/10.1109/icaccs.2015.7324119.

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Chengalvarayan, Rathinavelu. "The use of nonlinear energy transformation for Tamil connected-digit speech recognition." In 6th International Conference on Spoken Language Processing (ICSLP 2000). ISCA: ISCA, 2000. http://dx.doi.org/10.21437/icslp.2000-733.

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Radha, V., C. Vimala, and M. Krishnaveni. "Continuous Speech Recognition system for Tamil language using monophone-based Hidden Markov Model." In the Second International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2393216.2393255.

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Ram, C. Sunitha, and R. Ponnusamy. "An effective automatic speech emotion recognition for Tamil language using Support Vector Machine." In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2014. http://dx.doi.org/10.1109/icicict.2014.6781245.

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Madhavaraj, A., and A. G. Ramakrishnan. "Design and development of a large vocabulary, continuous speech recognition system for Tamil." In 2017 14th IEEE India Council International Conference (INDICON). IEEE, 2017. http://dx.doi.org/10.1109/indicon.2017.8488025.

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