A Theoretical Framework for Acoustic Neighbor Embeddings

Apple Machine Learning Journal / 4/9/2026

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Key Points

The paper “A Theoretical Framework for Acoustic Neighbor Embeddings” proposes a theoretical framework for learning embeddings based on acoustic neighbors.
It is published on arXiv (2412.02164) with an associated Apple GitHub repository, suggesting reproducible implementation details.
By focusing on “neighbor” structure in the acoustic domain, the approach aims to improve how audio representations capture relevant similarity.
The work targets speech and natural language processing use cases that rely on robust acoustic embeddings.
The publication and released source code may lower the barrier for researchers and engineers to experiment with neighbor-based acoustic representation learning.

This paper provides a theoretical framework for interpreting acoustic neighbor embeddings, which are representations of the phonetic content of variable-width audio or text in a fixed-dimensional embedding space. A probabilistic interpretation of the distances between embeddings is proposed, based on a general quantitative definition of phonetic similarity between words. This provides us a framework for understanding and applying the embeddings in a principled manner. Theoretical and empirical evidence to support an approximation of uniform cluster-wise isotropy are shown, which allows us to…

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