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

TL;DR

This paper introduces a novel deep convolutional Maxout neural network architecture for Persian speech recognition that improves accuracy and reduces training time by effectively localizing time-frequency information.

Contribution

The paper proposes the Time-Frequency Convolutional Maxout Neural Network (TFCMNN), a new model that enhances speech recognition performance and training efficiency over traditional methods.

Findings

TFCMNN achieves 1.6% higher recognition accuracy.

Training time is reduced by approximately 17 hours.

Time-frequency localization improves recognition and training speed.

Abstract

In this paper, a CNN-based structure for the time-frequency localization of information is proposed for Persian speech recognition. Research has shown that the receptive fields' spectrotemporal plasticity of some neurons in mammals' primary auditory cortex and midbrain makes localization facilities improve recognition performance. Over the past few years, much work has been done to localize time-frequency information in ASR systems, using the spatial or temporal immutability properties of methods such as HMMs, TDNNs, CNNs, and LSTM-RNNs. However, most of these models have large parameter volumes and are challenging to train. For this purpose, we have presented a structure called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) in which parallel time-domain and frequency-domain 1D-CMNNs are applied simultaneously and independently to the spectrogram, and then their outputs are concatenated and applied jointly 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 Dropout, maxout, and weight normalization. Two sets of experiments were designed and implemented on the FARSDAT dataset 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 1D-CMNN models. In addition, the average training time of the TFCMNN models is about 17 hours lower than the average training time of traditional models. Therefore, as proven in other sources, time-frequency localization in ASR systems increases system accuracy and speeds up the training process.