Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition

Abstract

Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. However, CNN is not suitable for modeling the long-term context dependencies between speech signal frames. Recently, the recurrent neural networks (RNNs) have shown great abilities for modeling long-term context dependencies. However, the performance of RNNs is not good for low-resource speech recognition tasks, and is even worse than the conventional feed-forward neural networks. Moreover, these networks often overfit severely on the training corpus in the low-resource speech recognition tasks. This paper presents the results of our contributions to combine CNN and conventional RNN with gate, highway, and residual networks to reduce the above problems. The optimal neural network structures and training strategies for the proposed neural network models are explored. Experiments were conducted on the Amharic and Chaha datasets, as well as on the limited language packages (10-h) of the benchmark datasets released under the Intelligence Advanced Research Projects Activity (IARPA) Babel Program. The proposed neural network models achieve 0.1–42.79% relative performance improvements over their corresponding feed-forward DNN, CNN, bidirectional RNN (BRNN), or bidirectional gated recurrent unit (BGRU) baselines across six language collections. These approaches are promising candidates for developing better performance acoustic models for low-resource speech recognition tasks.

The Multi-Domain International Search on Speech 2020 ALBAYZIN Evaluation: Overview, Systems, Results, Discussion and Post-Evaluation Analyses

“…This program focused on building fully automatic and noise-robust speech recognition and search systems in a very limited amount of time (e.g., one week) and with limited amount of training data. The languages addressed in that program were low-resourced, such as Cantonese, Pashto, Tagalog, Turkish, Vietnamese, Swahili, Tamil and so on, and significant research has been carried out [13,61,[147][148][149][150][151][152][153][154][155][156][157][158][159].…”

Extreme random forest method for machine fault classification

“…Deep neural networks (DNNs) are now deployed for variety of artificial intelligence (AI) applications including computer vision [1][2][3][4][5][6], speech recognition [7][8][9], medical detection [10][11][12][13][14][15], mechanical fault diagnosis [16][17][18][19][20][21][22], etc. And it has demonstrated phenomenal success (often beyond human capabilities) in solving complex problems.…”