Brazilian Electricity Regulatory Agency (ANEEL) defines as non-technical losses the problems that happen during the energy consumption measurement process. A feasible alternative to reduce those losses is the energy consumption reading process performed by customers, called self-reading. This process might be executed across digital platforms such as mobile applications and the customers would register and send consumption information. In this context, the consumption reading using voice is a simple way for the public that has lower affinity for technology to perform self-reading. Therefore, this work proposes an end-to-end speech recognition method applied to the energy consumption measurement that uses a neural network architecture based on Recurrent Neural Network Transducer (RNN-T). The proposed method uses a modified architecture called Residual Recurrent Neural Network Transducer (RRNN-T) which contains a Residual Skip that make its behavior similar to a network shallower than RNN-T by skipping some layers in the training process. That strategy saves computational cost of the inference process and disk space. A spelled sequence of digits is the input of the proposed method that recognizes those digits and outputs the electrical energy consumption reading measured in kilowatts per hour. The dataset used in this work contains 111,224 samples of spelled sequences divided into 70\% for training and 30\% for test. The proposed method obtains 0.05 word error rate (WER) and inference time less than 3 seconds on mobile devices.

Self-Reading by Speech: An End-to-end Speech Recognition Method Applied to the Energy Consumption Measurement on Mobile Devices

technical paper

The 27th International Conference on Systems, Signals and Image Processing.

IWSSIP 2020

UFMA

Italo Silva

1

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Self-Reading by Speech: An End-to-end Speech Recognition Method Applied to the Energy Consumption Measurement on Mobile Devices

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