Agricultural price information acquisition using noise-robust Mandarin auto speech recognition

Abstract

Speech-recognition technology was applied to collect agricultural-price information. In this paper, we propose a robust continuous Mandarin speech-recognition method suitable for environments where agricultural product prices are acquired. To mitigate the decrease in recognition rate caused by the mismatch between training and real tests, we developed acoustic models based on the Hidden Markov Model (HMM) and trained the models by collecting data in different environments. The results showed that the recognition performance of triphone models was superior to that of monophone models. Both male and female HMMs performed better than the male and female mixed acoustic models. Although the decision-tree clustering method could not significantly improve the recognition rate, it evidently would reduce the quantity of triphone models. Gaussian mixture components improved the recognition rate on one hand, but they increased the calculation tasks on the other hand. The cepstral mean normalization and cepstral variance normalization methods significantly improved the identification-system performance. Under different locations and different speaker tests, the methods we used demonstrated varying degrees of improvement in recognition performance. The ultimate recognition rates were 95.04% for the males and 97.62% for the females. Speech-recognition technology can possibly be applied to collection of agricultural-price information. The experimental results showed that the models trained by these methods exhibited good recognition performance. Furthermore, the approach adopted by our research lays the foundation for the development of an application system in the future.