Intelligent conference summary generation method and system
A meeting minutes and intelligent technology, applied in neural learning methods, biological neural network models, speech analysis, etc., can solve problems such as relying on the clerk's concentration and clerk's omission, so as to reduce the text processing process, reduce the workload, The effect of improving execution efficiency
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Embodiment 1
[0033] refer to figure 1 The schematic diagram shows the error correction process of a method for generating intelligent meeting minutes proposed in this embodiment. In order to find out the words that may be wrong in the text, including words with wrong fonts and wrong pronunciations, and then perform error correction, the current technology usually The error correction process adopted includes: error detection, using rules or deep models to detect errors in the text and forming a candidate set of suspected error positions, usually with word granularity and word granularity detection; error correction, for the candidate set of suspected error positions Generate a corresponding candidate set for each error point, and then replace and calculate ppl one by one to determine the correct word.
[0034] The current error correction methods mainly have the following problems: (1) In the process of using rules, ngram language models and other methods to detect wrong words, compared wi...
Embodiment 2
[0087] refer to Figure 2-6 For the sake of illustration, this embodiment proposes a system for generating intelligent meeting minutes, and the generation method of the foregoing embodiment is implemented based on this embodiment. Specifically, this embodiment includes a collection module 100 , a voiceprint feature extraction module 200 , a role separation module 300 , a voice transcription module 400 , a text processing module 500 and a summary generation module 600 .
[0088] Further, wherein the collection module 100 is used to obtain conference audio and video signals; the voiceprint feature extraction module 200 is connected to the collection module 100 for extracting the voiceprint features of real-time voice signals; The feature matching performed by the preset voiceprint database recognizes the voice signals of different speakers and separates the voice signals; the voice transcription module 400 is connected with the role separation module 300 and is used to transcrib...
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