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Scalable Identity-Oriented Speech Retrieval
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AI
DOI：10.1109/TKDE.2021.3127520.png)
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Abstract
En 中文
With the prevalence of voice devices in our daily life, speech data is accumulated at an unprecedented speed, forming an invaluable database for security surveillance and financial risk management. In these applications, a key task is given a querying speech snippet to retrieve all speech snippets that are uttered by the same speaker as the querying one, namely Identity-Oriented Speech Retrieval (IO-SR). In this paper, we propose an accuracy and scalable system for IO-SR, which seamlessly integrates speaker modeling and deep indexing techniques. Evaluations on an industrial dataset containing millions of speech snippets show that our system achieves superior performance compared with the state-of-the-art methods.
Keywords:
Feature extraction
Binary codes
Indexing
Memory management
Databases
Task analysis
Speech coding
Speech search
neural network
information retrieval
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