Unsupervised Speech Segmentation: A Comprehensive Exploration Using Speech LLMs
This paper presents a novel exploration into unsupervised speech segmentation, leveraging the capabilities of Speech LLMs (SLMs) to address segmentation tasks focused on acoustic-semantic differences that do not primarily translate into textual content, such as emotion and speaker identity. The paper extends beyond conventional segmentation approaches, which typically concentrate on spectral changes, by attempting to capture multiple acoustic-semantic style transitions in an unsupervised manner.
Methodological Overview
The approach introduced in this paper utilizes a pipeline consisting of three primary components: a sentencer, a scorer, and a span-selector. First, the audio input is divided into uniformly sized segments known as "acoustic-sentences." The scorer then applies a Point-Wise Mutual Information (PMI) scoring mechanism, quantifying the coherence between consecutive acoustic-sentences using probability distributions approximated by the SLM. The span-selector, finally, determines the optimal segmentation by either utilizing a fixed number of segments, adapting dynamically based on input characteristics, or applying a model threshold.
Empirical Evaluation
The authors conducted empirical evaluations using two benchmarks: EmoV-DB and IEMOCAP, generating datasets focusing on transitions in emotion and gender. Results indicate that the proposed method surpasses evaluated baselines, including state-of-the-art diarization methods for individual style changes, given its robustness across different acoustic-semantic segmentation tasks. Of particular note is the method's performance relative to speaker diarization software, even when silence in the transition points between concatenated audio segments was removed, suggesting that the approach generalizes well to diverse acoustic contexts.
Implications and Future Directions
From a theoretical perspective, the significance of this research lies in advancing SLMs' role in non-textual speech processing tasks. The ability to segment speeches into meaningful acoustic-semantic units potentially enhances various applications, including hierarchical speech modeling and spoken dialogue systems. Practically, the unsupervised nature of this approach presents a scalable solution for speech processing systems that do not rely on extensive manual annotations, which could be beneficial for deployment in languages or dialects with limited resources.
Future work could focus on addressing current limitations, such as optimizing the initial segmentation step and reducing inference times by refining SLM architectures. Furthermore, expanding the benchmarks to include more semantic style changes could yield additional insights into the model's capacity to handle complex acoustic transformations.
In conclusion, this paper contributes a substantial step toward generalized unsupervised speech segmentation, showcasing the potential of SLMs to process complex acoustic-semantic changes without reliance on text conversions. The method's adaptability across different style changes suggests its utility in broadening the scope and accuracy of speech processing technologies.