{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:51:36Z","timestamp":1763643096077},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Speech Technol"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s10772-021-09814-2","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T05:56:19Z","timestamp":1612936579000},"page":"517-527","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Enhancing accuracy of long contextual dependencies for Punjabi speech recognition system using deep LSTM"],"prefix":"10.1007","volume":"24","author":[{"given":"Virender","family":"Kadyan","sequence":"first","affiliation":[]},{"given":"Mohit","family":"Dua","sequence":"additional","affiliation":[]},{"given":"Poonam","family":"Dhiman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"9814_CR1","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems."},{"key":"9814_CR2","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265\u2013283)."},{"issue":"3","key":"9814_CR3","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1007\/s11235-011-9623-0","volume":"52","author":"RK Aggarwal","year":"2013","unstructured":"Aggarwal, R. K., & Dave, M. (2013). Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system. Telecommunication Systems, 52(3), 1457\u20131466.","journal-title":"Telecommunication Systems"},{"key":"9814_CR4","doi-asserted-by":"crossref","unstructured":"Bahl, L., Brown, P., De Souza, P., & Mercer, R. (1986, April). Maximum mutual information estimation of hidden Markov model parameters for speech recognition. In ICASSP'86. IEEE international conference on acoustics, speech, and signal processing (Vol. 11, pp. 49\u201352). IEEE.","DOI":"10.1109\/ICASSP.1986.1169179"},{"key":"9814_CR5","doi-asserted-by":"crossref","unstructured":"Bassan, N., & Kadyan, V. (2018). An experimental study of continuous automatic speech recognition system using MFCC with Reference to Punjabi. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, 1, 267.","DOI":"10.1007\/978-981-10-8639-7_28"},{"key":"9814_CR6","first-page":"1137","volume":"3","author":"Y Bengio","year":"2003","unstructured":"Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137\u20131155.","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"9814_CR7","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157\u2013166.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"16","key":"9814_CR8","first-page":"582","volume":"16","author":"W Chen","year":"2009","unstructured":"Chen, W., Zhenjiang, M., & Xiao, M. (2009). Comparison of different implementations of MFCC. Journal of Computer Science and Technology, 16(16), 582\u2013589.","journal-title":"Journal of Computer Science and Technology"},{"key":"9814_CR9","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1007\/s12652-018-0828-x","volume":"10","author":"M Dua","year":"2018","unstructured":"Dua, M., Aggarwal, R. K., & Biswas, M. (2018). GFCC based discriminatively trained noise robust continuous ASR system for Hindi language. Journal of Ambient Intelligence and Humanized Computing, 10, 2301\u20132314.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"4","key":"9814_CR10","first-page":"359","volume":"9","author":"M Dua","year":"2012","unstructured":"Dua, M., Aggarwal, R. K., Kadyan, V., & Dua, S. (2012). Punjabi automatic speech recognition using HTK. International Journal of Computer Science Issues (IJCSI), 9(4), 359.","journal-title":"International Journal of Computer Science Issues (IJCSI)"},{"key":"9814_CR11","first-page":"115","volume":"3","author":"FA Gers","year":"2002","unstructured":"Gers, F. A., Schraudolph, N. N., & Schmidhuber, J. (2002). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3, 115\u2013143.","journal-title":"Journal of Machine Learning Research"},{"key":"9814_CR12","unstructured":"Ghosh, S., Vinyals, O., Strope, B., Roy, S., Dean, T., & Heck, L. (2016). Contextual lstm (clstm) models for large scale nlp tasks. arXiv:1602.06291."},{"key":"9814_CR13","doi-asserted-by":"crossref","unstructured":"Gillick, D., Wegmann, S., & Gillick, L. (2012, March). Discriminative training for speech recognition is compensating for statistical dependence in the HMM framework. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4745\u20134748). IEEE.","DOI":"10.1109\/ICASSP.2012.6288979"},{"issue":"5\u20136","key":"9814_CR14","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5\u20136), 602\u2013610.","journal-title":"Neural Networks"},{"key":"9814_CR15","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A. R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645\u20136649). IEEE.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"9814_CR16","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645\u20136649). IEEE.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"9814_CR17","unstructured":"Hermans, M., & Schrauwen, B. (2013). Training and analysing deep recurrent neural networks. In Advances in neural information processing systems (pp. 190\u2013198)."},{"issue":"8","key":"9814_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"issue":"4","key":"9814_CR19","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.csl.2009.08.002","volume":"24","author":"H Jiang","year":"2010","unstructured":"Jiang, H. (2010). Discriminative training of HMMs for automatic speech recognition: A survey. Computer Speech & Language, 24(4), 589\u2013608.","journal-title":"Computer Speech & Language"},{"issue":"5","key":"9814_CR20","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1080\/03772063.2017.1369370","volume":"64","author":"V Kadyan","year":"2018","unstructured":"Kadyan, V., Mantri, A., & Aggarwal, R. K. (2018). Refinement of HMM model parameters for punjabi automatic speech recognition (PASR) system. IETE Journal of Research, 64(5), 673\u2013688.","journal-title":"IETE Journal of Research"},{"issue":"1","key":"9814_CR21","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s10772-018-09577-3","volume":"22","author":"V Kadyan","year":"2019","unstructured":"Kadyan, V., Mantri, A., Aggarwal, R. K., & Singh, A. (2019). A comparative study of deep neural network based Punjabi-ASR system. International Journal of Speech Technology, 22(1), 111\u2013119.","journal-title":"International Journal of Speech Technology"},{"key":"9814_CR22","doi-asserted-by":"crossref","unstructured":"Kipyatkova, I., & Karpov, A. (2015, November). Recurrent neural network-based language modeling for an automatic Russian speech recognition system. In 2015 Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference (AINL-ISMW FRUCT) (pp. 33\u201338). IEEE.","DOI":"10.1109\/AINL-ISMW-FRUCT.2015.7382966"},{"key":"9814_CR23","unstructured":"Kneser, R., Ney, H. (1995). Improved backing-off for M-gram language modeling. In: 1995 International Conference on Acoustics, Speech and Signal Processing (ICASSP) (vol. 1, pp. 181\u2013184)."},{"key":"9814_CR24","doi-asserted-by":"crossref","unstructured":"Kuamr, A., Dua, M., & Choudhary, A. (2014b). Implementation and performance evaluation of continuous Hindi speech recognition. In 2014 International Conference on Electronics and Communication Systems (ICECS) (pp. 1\u20135). IEEE.","DOI":"10.1109\/ECS.2014.6892777"},{"key":"9814_CR25","doi-asserted-by":"crossref","unstructured":"Kuamr, A., Dua, M., & Choudhary, T. (2014a). Continuous Hindi speech recognition using Gaussian mixture HMM. In 2014 IEEE Students' Conference on Electrical, Electronics and Computer Science (pp. 1\u20135). IEEE.","DOI":"10.1109\/SCEECS.2014.6804519"},{"issue":"1","key":"9814_CR26","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1515\/jisys-2018-0417","volume":"30","author":"A Kumar","year":"2020","unstructured":"Kumar, A., & Aggarwal, R. K. (2020). Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling. Journal of Intelligent Systems, 30(1), 165\u2013179.","journal-title":"Journal of Intelligent Systems"},{"key":"9814_CR27","doi-asserted-by":"crossref","unstructured":"Kumar, A., & Aggarwal, R. K. (2020b). A time delay neural network acoustic modeling for hindi speech recognition. In Advances in Data and Information Sciences (pp. 425\u2013432). Springer, Singapore.","DOI":"10.1007\/978-981-15-0694-9_40"},{"key":"9814_CR28","doi-asserted-by":"crossref","unstructured":"Medennikov, I., & Bulusheva, A. (2016). LSTM-based language models for spontaneous speech recognition. In International conference on speech and computer (pp. 469\u2013475). Springer, Cham.","DOI":"10.1007\/978-3-319-43958-7_56"},{"key":"9814_CR29","doi-asserted-by":"crossref","unstructured":"Medennikov, I., & Bulusheva, A. (2016, August). LSTM-based language models for spontaneous speech recognition. In International conference on speech and computer (pp. 469\u2013475). Springer, Cham.","DOI":"10.1007\/978-3-319-43958-7_56"},{"issue":"1","key":"9814_CR30","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1006\/csla.2001.0184","volume":"20","author":"M Mohri","year":"2002","unstructured":"Mohri, M., Pereira, F., & Riley, M. (2002). Weighted finite-state transducers in speech recognition. Computer Speech and Language, 20(1), 69\u201388.","journal-title":"Computer Speech and Language"},{"key":"9814_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01325-y","author":"V Passricha","year":"2019","unstructured":"Passricha, V., & Aggarwal, R. K. (2019). A comparative analysis of pooling strategies for convolutional neural network based Hindi ASR. Journal of Ambient Intelligence and Humanized Computing. https:\/\/doi.org\/10.1007\/s12652-019-01325-y.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"9814_CR32","unstructured":"Povey, D. (2005). Discriminative training for large vocabulary speech recognition. Doctoral dissertation, University of Cambridge."},{"key":"9814_CR33","doi-asserted-by":"crossref","unstructured":"Povey, D., & Woodland, P. (2001, May). Improved discriminative training techniques for large vocabulary continuous speech recognition. In 2001 IEEE International conference on acoustics, speech, and signal processing. proceedings (Cat. No. 01CH37221) (Vol. 1, pp. 45\u201348). IEEE.","DOI":"10.1109\/ICASSP.2001.940763"},{"key":"9814_CR34","doi-asserted-by":"crossref","unstructured":"Sahu, P., Dua, M., & Kumar, A. (2018). Challenges and issues in adopting speech recognition. In Speech and language processing for human-machine communications (pp. 209\u2013215). Springer, Singapore.","DOI":"10.1007\/978-981-10-6626-9_23"},{"key":"9814_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s10772-018-09573-7","author":"H Sak","year":"2014","unstructured":"Sak, H., Senior, A. W., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. International Journal of Speech Technology. https:\/\/doi.org\/10.1007\/s10772-018-09573-7.","journal-title":"International Journal of Speech Technology"},{"issue":"3","key":"9814_CR36","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1016\/j.csl.2006.09.003","volume":"21","author":"H Schwenk","year":"2007","unstructured":"Schwenk, H. (2007). Continuous space language models. Computer Speech & Language, 21(3), 492\u2013518.","journal-title":"Computer Speech & Language"},{"key":"9814_CR37","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Schl\u00fcter, R., & Ney, H. (2012). LSTM neural networks for language modeling. In Thirteenth annual conference of the international speech communication association.","DOI":"10.21437\/Interspeech.2012-65"},{"key":"9814_CR38","unstructured":"Tian, X., Zhang, J., Ma, Z., He, Y., Wei, J., Wu, P., & Zhang, Y. (2017). Deep LSTM for large vocabulary continuous speech recognition. https:\/\/arxiv.org\/abs\/1703.07090."},{"key":"9814_CR39","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Ravuri, S. V., & Povey, D. (2012, March). Revisiting recurrent neural networks for robust ASR. In 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4085\u20134088). IEEE.","DOI":"10.1109\/ICASSP.2012.6288816"},{"issue":"4","key":"9814_CR40","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1162\/neco.1990.2.4.490","volume":"2","author":"RJ Williams","year":"1990","unstructured":"Williams, R. J., & Peng, J. (1990). An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Computation, 2(4), 490\u2013501.","journal-title":"Neural Computation"},{"issue":"1","key":"9814_CR41","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1006\/csla.2001.0182","volume":"16","author":"PC Woodland","year":"2002","unstructured":"Woodland, P. C., & Povey, D. (2002). Large scale discriminative training of hidden Markov models for speech recognition. Computer Speech & Language, 16(1), 25\u201347.","journal-title":"Computer Speech & Language"},{"key":"9814_CR42","unstructured":"Woodland, P.C., & Povey D (2000) Large scale discriminative training for speech recognition. ASR2000-automatic speech recognition: challenges for the new millenium ISCA tutorial and research workshop (ITRW)."},{"key":"9814_CR43","doi-asserted-by":"crossref","unstructured":"Zeyer, A., Doetsch, P., Voigtlaender, P., Schl\u00fcter, R., & Ney, H. (2017, March). A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 2462\u20132466). IEEE.","DOI":"10.1109\/ICASSP.2017.7952599"}],"container-title":["International Journal of Speech Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-021-09814-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10772-021-09814-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-021-09814-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T23:36:49Z","timestamp":1724456209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10772-021-09814-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,9]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["9814"],"URL":"https:\/\/doi.org\/10.1007\/s10772-021-09814-2","relation":{},"ISSN":["1381-2416","1572-8110"],"issn-type":[{"type":"print","value":"1381-2416"},{"type":"electronic","value":"1572-8110"}],"subject":[],"published":{"date-parts":[[2021,2,9]]},"assertion":[{"value":"7 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}