Translation Optimization Technology of Automatic Speech Recognition Based on Industry-Specific Vocabulary

Automatic speech recognition (ASR) technology has been developed relatively mature, and general ASR engines have been widely used in transportation, medical, communication and other industries. However, due to non-independent homology of industry-specific vocabulary in the large-scale training corpus, there comes to low recognition accuracy of industry-specific vocabulary when the general ASR engines are applied to various subdivisions of industries. As compared with 16 kHz audio sampling rate in Internet environment, narrowband low sampling (8 kHz) of call center may result in more significant decrease of recognition accuracy of ASR. In order to improve the accuracy of speech recognition of industry-specific words, this paper proposes a translation optimization technology of ASR based on industry-specific vocabulary. Specifically, first, convolutional neural network model and deep neural network BERT model are used to predict word for corpus text data, and an industry-specific error correction vocabulary is generated. Next, in the production environment, a general ASR engine is used to perform initial transcription of telephone call voice data. Then, the transcribed text is corrected by using the Soft-Masked BERT model combined with the industry-specific error correction vocabulary, thus improving the accuracy of speech recognition. Finally, by using 12345 hotline customer service call voice data for modeling and testing, the proposed translation optimization technology is proved efficient in improving the accuracy of general ASR recognition by 10 percentage points with high error correction speed and good applicability.