Continuous Bengali Speech Recognition Based On Deep Neural Network

Amin, Md. Alif Al; Islam, Md. Saiful; Kibria, Shafkat; Rahman, Md. Saidur

doi:10.1109/ecace.2019.8679341

Cited by 19 publications

(6 citation statements)

References 14 publications

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“…They have achieved a word detection error rate of 13.2% and phoneme detection error rate of 28.7% on the Bangla-RealNumber audio dataset. Al Aminet al [10] used DNN-HMM and GMM-HMM-based models, which have been implemented in the Kaldi toolkit, for continuous Bengali speech recognition benchmarking on a standard and publicly published corpus called SHRUTI. The study has been shown using Kaldi-based feature extraction recipes with DNN-HMM and GMM-HMM acoustic models have achieved performances WER 0.92% and WER 2.02%.…”

Section: Banglamentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

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Speech recognition of a language is a key area in the field of pattern recognition. This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics. An immense number of frameworks are available for speech processing and recognition for languages persisting around the globe. However, a limited number of automatic speech recognition systems are available for commercial use. The gap between the languages being spoken around the globe and the technical support available to these languages are very few. This paper examined major challenges for speech recognition for different languages. Analysis of the literature shows that lack of standard databases availability of minority languages hinder the research recognition research across the globe. When compared with non-Indian languages, the research on speech recognition of Indian languages (except Hindi) has not achieved the expected milestone yet. Combination of MFCC and DNN–HMM classifier is most commonly used system for developing ASR minority languages, whereas in some of the majority languages, researchers are using much advance algorithms of DNN. It has also been observed that the research in this field is quite thin and still more research needs to be carried out, particularly in the case of minority languages.

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Section: Banglamentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

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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]. Numerous toolkits are accessible to the researchers in the area of SR namely Sphinx, HTK, Julius and Kaldi.…”

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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“…(1)(2)(3) . The SR related study in Hindi using Kaldi is documented in (4)(5)(6) . The research on continuous Hindi SR is performed by a research unit (7) .…”

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 Bengali Speech Recognition Based On Deep Neural Network

Cited by 19 publications

References 14 publications

Computational intelligence in processing of speech acoustics: a survey

Computational intelligence in processing of speech acoustics: a survey

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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