This work proposes a novel architecture to detect user-defined flexible keywords in real-time. The approach involves constructing acoustic embeddings of keywords using graphene-to-phone conversion, and converting phone-to-embedding by looking up the embedding dictionary built during training. The key benefit is the incorporation of both text and audio embedding.
Flexible Keyword Spotting with Homogeneous Audio-Text Embedding
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Our approach involves constructing a representative acoustic embedding of a keyword through graphene-to-phone conversion. This is followed by a phone-to-embedding conversion, which is done by looking up the embedding dictionary built by averaging the corresponding embeddings from the audio encoder of each phone during training. The key benefit of our approach is that it combines both text and audio-based techniques to enable accurate and real-time keyword spotting.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding
- Apple Machine Learning Research
- Twitter – @itinaicom
