[b]=[d]-[t]+[p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic
Abstract
Self-supervised speech models (S3Ms) are known to encode rich phonetic information, yet how this information is structured remains underexplored. We conduct a comprehensive study across 96 languages to analyze the underlying structure of S3M representations, with particular attention to phonological vectors. We first show that there exist linear directions within the model's representation space that correspond to phonological features. We further demonstrate that the scale of these phonological vectors correlate to the degree of acoustic realization of their corresponding phonological features in a continuous manner. For example, the difference between [d] and [t] yields a voicing vector: adding this vector to [p] produces [b], while scaling it results in a continuum of voicing. Together, these findings indicate that S3Ms encode speech using phonologically interpretable and compositional vectors, demonstrating phonological vector arithmetic. All code and interactive demos are available at https://github.com/juice500ml/phonetic-arithmetic .
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the demonstration that self-supervised speech models encode phonologically interpretable and compositional vectors, revealing a structured representation of phonological features. This work significantly advances the understanding of S3M representations and their potential applications in speech technology and linguistics.
Methodology
The paper presents a novel approach to understanding self-supervised speech models (S3Ms) by investigating the linear structure of phonological features within their representation space. The methodology involves analyzing phonological vectors across 96 languages, establishing a framework for phonological vector arithmetic. The use of cosine similarity to evaluate phonological analogies and the introduction of a vocoder to assess the scaling of phonological vectors are innovative aspects that enhance the understanding of S3M representations.
Results & evaluation
The experiments are well-structured, utilizing two datasets (TIMIT and VoxAngeles) to validate the hypotheses regarding phonological vector arithmetic and scaling. The results demonstrate a strong correlation between the scale of phonological vectors and acoustic measurements, providing empirical support for the proposed theories. The analysis of phonological features across different languages adds to the robustness of the findings, although the paper could benefit from a broader range of S3Ms to validate the generalizability of the results.
Reproducibility
The authors have made their code and interactive demos publicly available, which is a positive aspect for reproducibility. However, the paper could improve by providing more detailed implementation specifics, particularly regarding the training of the vocoder and the exact configurations used for the S3Ms.
Limitations
The study is limited by its focus on a specific set of phonological features as defined by PanPhon, which may not capture the full complexity of phonological systems across all languages. Additionally, the results are influenced by the choice of vocoder, and the authors acknowledge that different vocoders may yield varying synthesis results. The paper also notes that it does not explore all possible S3Ms, which could limit the generalizability of the findings.
Broader impact
The findings have significant implications for both speech processing and linguistic theory. By demonstrating that S3Ms can learn interpretable phonological structures, the research opens avenues for more intuitive speech synthesis and understanding of phonological features as continuous rather than binary. This could enhance applications in speech recognition, synthesis, and language learning technologies. The main contribution of this paper is the demonstration that self-supervised speech models encode phonologically interpretable and compositional vectors, revealing a structured representation of phonological features. This work significantly advances the understanding of S3M representations and their potential applications in speech technology and linguistics.
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