Abstract
To make the best use of speech recognition, it is imperative that it can recognize not just speech or speaker, but also the domain of communication. This paper proposes an approach for recognition of the acoustic domain using ensemble-based 3-level architecture instead of a single classifier for training and testing. It is estimated the predictions of various classifiers and then selects a set of three classifiers such that, any of the three classifiers must contain the target predictions and finally, these predictions are used to train another random forest classifier. It yields the final classification results of test data set. Experimental results indicate that the proposed method has consistent performance even if data size is increased with acceptable accuracy i.e. 76.36%.
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Rathor, S., Jadon, R.S. Acoustic domain classification and recognition through ensemble based multilevel classification. J Ambient Intell Human Comput 10, 3617–3627 (2019). https://doi.org/10.1007/s12652-018-1087-6
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DOI: https://doi.org/10.1007/s12652-018-1087-6