Automatic speech recognition of Arabic multi-genre broadcast media

Abstract

This paper describes an Arabic Automatic Speech Recognition system developed on 15 hours of Multi-Genre Broadcast (MGB-3) data from YouTube, plus 1,200 hours of Multi-Dialect and Multi-Genre MGB-2 data recorded from the Aljazeera Arabic TV channel. In this paper, we report our investigations of a range of signal pre-processing, data augmentation, topic-specific language model adaptation, accent specific re-training, and deep learning based acoustic modeling topologies, such as feed-forward Deep Neural Networks (DNNs), Time-delay Neural Networks (TDNNs), Long Short-term Memory (LSTM) networks, Bidirectional LSTMs (BLSTMs), and a Bidirectional version of the Prioritized Grid LSTM (BPGLSTM) model. We propose a system combination for three purely sequence trained recognition systems based on lattice-free maximum mutual information, 4-gram language model re-scoring, and system combination using the minimum Bayes risk decoding criterion. The best word error rate we obtained on the MGB-3 Arabic development set using a 4-gram re-scoring strategy is 42.25% for a chain BLSTM system, compared to 65.44% baseline for a DNN system.