Now showing items 1-6 of 6
| Abstract: | Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data. |
| Description: | International Journal of Computer Applications (0975 – 8887) Volume 19– No.5, April 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4200 |
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| Automated Trans ... for Malayalam Language.pdf | (207.7Kb) |
| Abstract: | A primary medium for the human beings to communicate through language is Speech. Automatic Speech Recognition is wide spread today. Recognizing single digits is vital to a number of applications such as voice dialling of telephone numbers, automatic data entry, credit card entry, PIN (personal identification number) entry, entry of access codes for transactions, etc. In this paper we present a comparative study of SVM (Support Vector Machine) and HMM (Hidden Markov Model) to recognize and identify the digits used in Malayalam speech. |
| URI: | http://dyuthi.cusat.ac.in/purl/4232 |
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| A Comparative s ... alam Digit Recognition.pdf | (200.2Kb) |
| Abstract: | A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer forMalayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data. |
| Description: | Sadhana Vol. 38, Part 6, December 2013, pp. 1339–1346 |
| URI: | http://dyuthi.cusat.ac.in/purl/4227 |
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| Connected digit ... for Malayalam language.pdf | (242.7Kb) |
| Abstract: | Performance of any continuous speech recognition system is dependent on the accuracy of its acoustic model. Hence, preparation of a robust and accurate acoustic model lead to satisfactory recognition performance for a speech recognizer. In acoustic modeling of phonetic unit, context information is of prime importance as the phonemes are found to vary according to the place of occurrence in a word. In this paper we compare and evaluate the effect of context dependent tied (CD tied) models, context dependent (CD) and context independent (CI) models in the perspective of continuous speech recognition of Malayalam language. The database for the speech recognition system has utterance from 21 speakers including 11 female and 10 males. Our evaluation results show that CD tied models outperforms CI models over 21%. |
| Description: | Procedia Engineering,vol 30,pp 1081-1088 |
| URI: | http://dyuthi.cusat.ac.in/purl/4211 |
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| Development & e ... ous speech recognition.pdf | (444.9Kb) |
| Abstract: | Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data |
| Description: | International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4214 |
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| Malayalam Isola ... P cepstral coefficient.pdf | (172.8Kb) |
| Abstract: | Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task. |
| Description: | Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on |
| URI: | http://dyuthi.cusat.ac.in/purl/4190 |
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| Speech Recognition of Malayalam Numbers.pdf | (243.0Kb) |
Now showing items 1-6 of 6
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