Voice File Punctuation via Silence Segmentation and Semantic Weighting
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Solution Overview
Problem
Current methods for adding punctuation to voice files are time-consuming, lack intuition, and result in low accuracy due to parsing individual words and characters without considering the internal structural features or semantic relationships, leading to inaccurate translation and meaning representation.
Innovation Solution
A method and system that utilize silence or pause duration detection to divide voice files into speech segments, identify semantic features of terms and expressions, and apply a linguistic model to determine the weight of punctuation modes based on these features for accurate punctuation addition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the prior art method parses out every word or character and matches character location to each sentence, then the punctuation can be added based on linguistic model, but the process is time consuming and lacks intuition
Solution Approach 1:
The patent divides the voice file into multiple speech segments based on silence or pause duration detection, rather than processing the entire voice file as a single unit. This segmentation allows the system to focus computational resources on relevant portions, improving both accuracy and efficiency in punctuation addition.
Solution Approach 2:
The patent extracts semantic features and structural features from speech segments to identify key characteristics that indicate punctuation locations. By taking out only the essential features needed for punctuation determination, the system avoids processing unnecessary data, reducing time consumption while maintaining accuracy.
2Reliability
If the linguistic model is built using the location of individual word or character, then the model can be constructed, but it is limited in relevance and cannot extract actual relationship between information and punctuation mode
Solution Approach 1:
The patent transitions from a one-dimensional approach (individual word/character location) to a multi-dimensional approach by incorporating semantic features, structural features, and speech segment information. This dimensional expansion allows the model to capture complex relationships between information and punctuation modes that cannot be represented by simple position-based features.
Solution Approach 2:
The patent creates a composite feature set by combining multiple types of features (semantic, structural, acoustic) to build a more robust linguistic model. This composite approach enables the model to leverage complementary information from different feature types, improving punctuation accuracy while maintaining manageable complexity through integrated processing.
3Ease of operation
If the voice file is taken as a whole for punctuation addition, then processing is simplified, but the internal structural features are not considered resulting in low accuracy
Solution Approach 1:
The patent segments the voice file into speech units based on silence or pause durations, preserving internal structural features while maintaining manageable processing complexity. Each segment is processed independently to identify punctuation opportunities, then results are integrated to produce the final punctuated output.
Solution Approach 2:
The patent performs preliminary detection of silence or pause durations to identify potential punctuation locations before conducting detailed linguistic analysis. This preliminary action guides subsequent processing by highlighting regions of interest, simplifying the overall operation while improving accuracy through targeted analysis.
Data Source
AI summary
A method and system for adding punctuation to a voice file is disclosed. The method includes: utilizing silence or pause duration detection to divide a voice file into a plurality of speech segments for processing, the voice file includes a plurality of features units; identifying all features units that appear in the voice file according to every term or expression and semantics features of the every term or expression that form each of the plurality of speech segments; using a linguistic model to determine a sum of weight of various punctuation modes in the voice file according to all the feature units, the linguistic model is built upon semantics features of various parsed out terms or expressions from a body text of a spoken sentence according to a language library; and adding punctuations to the voice file based on the determined sum of weight of the various punctuation modes.