Time-Frequency Localization Using Deep Convolutional Maxout Neural Network in Persian Speech Recognition

In this paper, a CNN-based structure for time-frequency localization of audio signal information in the ASR acoustic model is proposed for Persian speech recognition. Research has shown that the receptive fields' time-frequency flexibility in some mammals' auditory neurons system improves recognition performance. Biosystems have inspired many artificial systems because of their high efficiency and performance, so time-frequency localization has been used extensively to improve system performance. In the last few years, much work has been done to localize time-frequency information in ASR systems, which has used the spatial immutability properties of methods such as TDNN, CNN and LSTM-RNN. However, most of these models have large parameter volumes and are challenging to train. In the structure we have designed, called Time-Frequency Convolutional Maxout Neural Network (TFCMNN), two parallel blocks consisting of 1D-CMNN each have weight sharing in one dimension, are applied simultaneously but independently to the feature vectors. Then their output is concatenated and applied to a fully connected Maxout network for classification. To improve the performance of this structure, we have used newly developed methods and models such as the maxout, Dropout, and weight normalization. Two experimental sets were designed and implemented on the Persian FARSDAT speech data set to evaluate the performance of this model compared to conventional 1D-CMNN models. According to the experimental results, the average recognition score of TFCMNN models is about 1.6% higher than the average of conventional models. In addition, the average training time of the TFCMNN models is about 17 hours lower than the average training time of traditional models. As a result, as mentioned in other references, time-frequency localization in ASR systems increases system accuracy and speeds up the model training process.