Transformer-Based Semantic Search for Long Audio Content
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Solution Overview
Problem
Current search systems are inadequate for handling long media items like podcasts, as they struggle to generate fixed-length embeddings for arbitrary-length strings, leading to inaccurate search results.
Innovation Solution
Generating topic embeddings for media items and queries using a transformer model, allowing for semantic search by identifying the top N topics and using K-nearest neighbors (KNN) to return relevant results, independent of language or speaker count.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search systems are used to handle long media items, then the system can process arbitrary-length strings, but the search accuracy deteriorates due to inability to generate fixed-length embeddings
Solution Approach 1:
The patent segments long media items into multiple fixed-size windows or segments. Each segment is processed independently to generate embeddings, which are then aggregated (e.g., through averaging or pooling) to create a fixed-length representation of the entire media item. This allows the system to handle arbitrary-length inputs while maintaining consistent embedding dimensions for accurate search.
Solution Approach 2:
The patent transforms the variable-length string data into a fixed-dimensional vector space through embedding layers. By mapping tokens to fixed-dimensional vectors and aggregating them through attention mechanisms or pooling operations, the system converts arbitrary-length sequences into consistent fixed-length embeddings suitable for similarity search.
2Productivity
If lexical search based on historical interaction data is used, then the system can retrieve preferred items efficiently, but user exploration and item discovery are limited
Solution Approach 1:
The patent introduces semantic embeddings as an intermediary between user queries and media items. Instead of directly matching lexical terms or relying solely on historical interaction patterns, the system uses semantic representations that capture the meaning and context of both queries and media content, enabling more effective exploration and discovery beyond what traditional lexical search can achieve.
Data Source
AI summary
The various implementations described herein include methods and devices for facilitating semantic search. In one aspect, a method includes obtaining audio content and extracting vocabulary terms from the audio content. The method further includes generating, using a transformer model, a vocabulary embedding from the vocabulary terms, and generating one or more topic embeddings from the audio content and the vocabulary embeddings. The method also includes generating a topic embedding index for the audio content based on the one or more topic embeddings, and storing the embedding index for use with a search engine system.