Audio Chunk Embedding for Synthetic Speech Detection
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Solution Overview
Problem
Conventional audio analysis systems struggle to accurately detect synthetic audio content due to their inability to process large and complex recordings, overlook subtle manipulations, and lack the capability to extract intricate audio characteristics, leading to misclassifications and inefficiencies in real-time applications.
Innovation Solution
A system utilizing a transformer encoder model to generate embeddings of audio chunks based on extracted features, followed by a classification model to distinguish between real and synthetic portions, with a user-friendly interface for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems process entire audio files as a whole, then they can handle complete recordings, but they cannot detect subtle manipulations and have limited ability to process large and complex audio recordings
Solution Approach 1:
The audio file is divided into smaller chunks that are processed individually. This segmentation allows the system to handle large audio recordings by processing manageable segments, improving both detection accuracy for subtle manipulations and overall processing capability without overwhelming complexity
2Measurement precision
If conventional systems analyze entire audio recordings comprehensively, then they can process complete data, but processing times increase and computational costs rise
Solution Approach 1:
By dividing the audio file into smaller chunks for independent processing, the system reduces the computational load on each processing unit, thereby decreasing overall processing time while maintaining comprehensive analysis capability through sequential evaluation of all chunks
Solution Approach 2:
The system processes only the necessary portions of the audio file (chunks) rather than requiring complete analysis of the entire recording, allowing for efficient processing that identifies synthetic portions without unnecessary computational overhead
3Measurement precision
If conventional systems use basic audio feature processing, then they can handle simple audio, but they cannot extract intricate characteristics needed to identify synthetic audio
Solution Approach 1:
The system transforms audio data into different parameter representations (embeddings) through the transformer encoder model, converting raw audio features into a compressed representation that captures intricate characteristics necessary for identifying synthetic audio while managing processing complexity
4Reliability
If conventional systems lack advanced classification models, then they are simpler to implement, but they misclassify synthetic audio and provide inaccurate results
Solution Approach 1:
The transformer encoder model acts as an intermediary that processes audio chunks and generates embeddings, which are then fed to the classification model. This intermediary layer extracts and compresses intricate audio characteristics, enabling accurate synthetic audio detection while separating the complexity of feature extraction from the classification function
Data Source
AI summary
The present subject matter relates to a system (100) and a method (300) for audio analysis. The system (100) is configured to receive an input audio file. Further, the system (100) is configured to pre-process the received input audio file and split the pre-processed input audio file into one or more chunks. Furthermore, the system (100) is configured to extract one or features corresponding to each chunk from the one or more chunks. Moreover, the system (100) generates embedding of each chunk from the one or more chunks based on the one or more features. Additionally, the system (100) classifies each chunk based on the generated embeddings, determining whether each chunk is real or synthetic. The system (100) provide information of each classified chunk to the user as either the real chunk or the synthetic chunk. The system enhances the ability to identify synthetic portions in artificially manipulated audio content.