Detecting user-defined flexible keywords in real-time can be difficult, especially when the keyword is in text form. However, a new architecture has been proposed to efficiently detect these keywords using a unique approach. By constructing representative acoustic embeddings of keywords, this architecture allows for more accurate semantic comparison. This means that it is able to find the most likely keyword from a user-defined list in a more precise way.
The architecture works by converting the keyword into an acoustic embedding using graphene-to-phone conversion. Then, a phone-to-embedding conversion is done by looking up the embedding dictionary, which is built by averaging the corresponding embeddings of each phone during training.
The key benefit of this approach is that both text embedding and audio embedding are in the same space, allowing for more accurate semantic comparison. This is in contrast to using independent text encoders, which may not be as accurate. This nearest neighbor search in the embedding space helps to find the most likely keyword from the user-defined flexible keyword list.
Overall, this novel architecture allows for the efficient detection of flexible keywords in real-time, making it a valuable tool in the world of AI and text analysis.
Keyword Detection Architecture
The architecture efficiently detects user-defined flexible keywords through a unique approach to constructing representative acoustic embeddings of keywords.
Benefits of the Approach
The benefit of this approach is that both text embedding and audio embedding are in the same space, allowing for more accurate semantic comparison. This is in contrast to using independent text encoders, which may not be as accurate.
Enhanced Semantic Comparison
This architecture allows for an enhanced semantic comparison, allowing for the more precise detection of user-defined flexible keywords in real-time.