Speaker Identification Using Locality Sensitive Hashing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Speaker identification systems face challenges in efficiently processing large datasets, requiring fast and accurate identification of speakers amidst vast amounts of data, particularly in web-scale applications where latency and scalability are critical.
Innovation Solution
The integration of i-vector-based speaker identification systems with locality sensitive hashing (LSH) enables fast nearest-neighbor search by approximating cosine distances, utilizing hash functions to efficiently compare and match speaker vectors, thereby reducing retrieval time while maintaining high identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear search is used to compare speaker vectors in a large database, then identification accuracy is maintained, but retrieval time and computational cost increase significantly
Solution Approach 1:
The patent segments the large speaker database into multiple hash tables, where each hash table stores speaker vectors hashed by different hash functions. This segmentation allows the system to search only relevant portions of the database rather than performing a complete linear search, significantly reducing retrieval time while maintaining identification accuracy through multiple hash function verification.
Solution Approach 2:
The patent performs preliminary hashing of speaker vectors into hash tables before the actual identification process. By pre-organizing speaker vectors using hash functions and storing them in hash tables with their speaker identities, the system eliminates the need for exhaustive linear searching during runtime, achieving fast retrieval without sacrificing accuracy.
2Adaptability or versatility
If the database of speakers is expanded to include more potential speaker identities, then identification coverage is improved, but computation and time required to identify the correct speaker increases
Solution Approach 1:
The patent introduces a new dimensional organization by hashing speaker vectors into multiple hash tables using different hash functions. This dimensional transformation allows the system to efficiently manage and search through expanded speaker databases by distributing vectors across multiple tables, maintaining both comprehensive coverage and fast processing speeds through intelligent data organization.
3Measurement precision
If more hash functions are used to improve search accuracy, then retrieval precision increases, but computational complexity increases
Solution Approach 1:
The patent applies partial hashing by using multiple hash functions only where necessary to achieve sufficient retrieval precision. Rather than applying all possible hash functions to every query, the system strategically selects and applies a subset of hash functions based on the specific search requirements, balancing retrieval precision with computational efficiency by avoiding unnecessary computational overhead.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing speaker identification. In some implementations, data identifying a media item including speech of a speaker is received. Based on the received data, one or more other media items that include speech of the speaker are identified. One or more search results are generated that each reference a respective media item of the one or more other media items that include speech of the speaker. The one or more search results are provided for display.