Voice Punctuation Accuracy via Silence Segmentation and Semantic Weights
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Solution Overview
Problem
Conventional methods for adding punctuations to voice files have low accuracy due to limited information used in establishing language models, which does not effectively capture the relationship between sentence information and punctuation states, and neglects the internal structural characteristics of voice files.
Innovation Solution
A method and system that identify feature units in voice files based on semantic features, divide the files into segments using silence detection, and apply a language model for weighted calculation to determine accurate punctuation states, incorporating both whole-file and segment-level semantic features for precise punctuation addition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional character separation and position-based language model is used, then the processing is simple, but the punctuation addition accuracy is low
Solution Approach 1:
The voice file is segmented into multiple segments based on silence detection, and feature units are identified both in the whole file and in each segment. This segmentation allows the system to capture local structural characteristics while maintaining overall context, thereby improving punctuation addition accuracy without excessive complexity increase
Solution Approach 2:
The system transitions from a single-dimensional character position approach to a multi-dimensional approach by incorporating both whole-file and segment-level feature units, along with semantic features and aggregate weights. This dimensional expansion enables the language model to capture relationships between sentence information and punctuation states more effectively
2Loss of information
If language model is established based on limited character position information, then the model is simple, but it cannot effectively extract the relationship between sentence information and punctuation states
Solution Approach 1:
The system merges multiple types of information including whole-file feature units, segment-level feature units, semantic features, and aggregate weights into a comprehensive language model. This merging allows the model to effectively extract relationships between sentence information and punctuation states by integrating diverse data sources
Solution Approach 2:
Feature units are identified and aggregate weights are calculated in advance before punctuation addition. The language model is pre-established with comprehensive training data that includes various feature combinations, enabling it to accurately determine punctuation states when processing voice files
3Measurement precision
If the voice file is processed as a whole without considering internal structure, then the processing is simple, but the punctuation addition accuracy is low
Solution Approach 1:
The voice file is divided into segments based on silence detection, allowing the system to analyze internal structural characteristics while maintaining overall context. This segmentation enables accurate punctuation addition by considering both local and global features without excessive processing complexity
Data Source
AI summary
Systems and methods are provided for adding punctuations. For example, one or more first feature units are identified in a voice file taken as a whole; the voice file is divided into multiple segments by detecting silences in the voice file; one or more second feature units are identified in the voice file; a first aggregate weight of first punctuation states of the voice file and a second aggregate weight of second punctuation states of the voice file are determined, using a language model established based on word separation and third semantic features; a weighted calculation is performed to generate a third aggregate weight based on a linear combination associated with the first aggregate weight and the second aggregate weight; and one or more final punctuations are added to the voice file based on at least information associated with the third aggregate weight.