Continuous Tamil Speech Recognition technique under non stationary noisy environments

Kalamani, M.; Makkithaya, Krishnamoorthi; Valarmathi, R.

doi:10.1007/s10772-018-09580-8

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…The MFCC feature vectors are clustered using Fuzzy C-Means technique and are used to build FCM EM GMM, K-means EM GMM, FCM-HMM, K Means HMM acoustic models. The FCM EM-GMM outperforms all other models [14].…”

Section: Literature Surveymentioning

confidence: 86%

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

2019

ijrte

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A digit recognition system is built for recognizing the sequence of digits through 0-9. The system is experimented with speech corpus created in the room environment. The acoustic information to feature representation is achieved using PLP and MFCC features. The system initially utilized the conventional GMM-HMM framework, state of the art hybrid classifier with varied number of states to complete the speech recognition task, i.e., the system is first trained and tested using Monophone models, and system’s recognition accuracy is then evaluated using Triphone Models: Triphone1 models, which was later followed by Triphones2 models and Triphones3 Models. The Ngram Language model is used for both Monophone and Triphone training. The system performance is evaluated with the use of MFCC and PLP parameterisation techniques on Kaldi toolkit. The system performance is evaluated using metrics word error rate (WER) and Word Recognition Accuracy (WRA). The proposed system can be utilized for building speech applications

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Section: Literature Surveymentioning

confidence: 86%

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

2019

ijrte

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“…Numerous experiments have been conducted in the recognizance of dialects such as Punjabi, Hindi, Tamil, Telugu, Kannada and so on [8] [9] [10]. The research related to SR in Hindi utilizing kaldi is accounted in [11] [12] [13].…”

Section: Literature Surveymentioning

confidence: 99%

Creation and Instigation of Triphone based Big-Lexicon Speaker-Independent Continuous Speech Recognition Framework for Kannada Language

2019

IJITEE

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This paper proposes a framework that is intended to do the comparably accurate recognition of speech and in precise, continuous speech recognition (CSR) based on triphone modelling for Kannada dialect. For designing the proposed framework, the features from the speech data are obtained from the well-known feature extraction technique Mel-frequency cepstral coefficients (MFCC) and from its transformations, like, linear discriminant analysis (LDA) and maximum likelihood linear transforms (MLLT) are obtained from Kannada speech data files. At that point, the system is trained to evaluate the hidden Markov model (HMM) parameters for continuous speech (CS) data. The persistent Kannada speech information is gathered from 2600 speakers (1560 men and 1040women) of the age bunch in the scope of 14 years-80 years. The speech information is acquired from different geographical regions of the Karnataka (one of the 29 states situated in the southern part of India) state under degraded condition. It comprises of 21,551 words that spread 30 locales. The performance evaluation of both monophone and triphone models concerning word error rate (WER) is done and the obtained results are compared with the standard databases such as TIMIT and aurora4. A significant reduction in WER is obtained for triphone models. The speech recognition (SR) rate is verified for both offline and online recognition mode for all the speakers. The results reveal that the recognition rate (RR) for Kannada speech corpus has got a better improvement over the state-of-the-art existing databases.

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“…Numerous studies have been performed with the identification different languages like Kannada, Punjabi, Tamil, Hindi, Telugu, etc. (1)(2)(3) . The SR related study in Hindi using Kaldi is documented in (4)(5)(6) .…”

Section: Introductionmentioning

confidence: 99%

Development of Speaker-Independent Automatic Speech Recognition System for Kannada Language

Prabhash¹,

Jayanna²

2022

IJST

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Objectives:The primary goal is to address attempts to establish a Continuous Speech Recognition (CSR) framework for recognising continuous speech in Kannada. It is a difficult challenge to deal with a local language such as Kannada, which lacks the resources of a single language database. Methods: Modelling techniques such as monophone, triphone, deep neural network (DNN)-hidden Markov model (HMM) and Gaussian Mixture Model (GMM)-HMM-based models were implemented in Kaldi toolkit and used for continuous Kannada speech recognition (CKSR). To extract feature vectors from speech data, the Mel frequency Cepstral (MFCC) coefficient technique is used. The continuous Kannada speech database consists of 2800 speakers (1680 males and 1120 females) belong to the age group 8 years to 80 years. The training and testing data are in the ratio 80:20. In this paper the hybrid modelling techniques are implemented to recognize the spoken words. Findings: The model efficiency is determined based on the word error rate (WER) and the obtained results are assessed with the well-known datasets such as TIMIT and Aurora-4. This study found that using Kaldi-based features ex-traction recipes for monophone, triphone, DNN-HMM and GMM-HMM acoustic models had a word error rate (WER) of 8.23%, 5.23%, 4.05% and 4.64% respectively. The experimental results suggest that the rate of recognition of Kannada speech data has increased higher than that of state-of-the-art databases. Novelty : We propose a novel automatic speech recognition system for Kannada language. The main reason for developing the automatic speech recognition system for Kannada language is that there are only limited sources of standard continuous Kannada speech are available. We created large vocabulary Kannada database. We implemented monophone, triphone, Subspace Gaussian mixture model (SGMM) and hybrid modelling techniques to develop the automatic speech recognition system for Kannada language.

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Continuous Tamil Speech Recognition technique under non stationary noisy environments

Cited by 9 publications

References 37 publications

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

Creation and Instigation of Triphone based Big-Lexicon Speaker-Independent Continuous Speech Recognition Framework for Kannada Language

Development of Speaker-Independent Automatic Speech Recognition System for Kannada Language

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