A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech
Summary: arXiv:2604.06327v1 Announce Type: cross
Recent advancements in diffusion-based text-to-speech (TTS) models have significantly improved the naturalness and expressiveness of synthesized speech. However, these models often encounter a critical issue known as speaker drift. This phenomenon refers to a subtle, yet noticeable, shift in the perceived identity of the speaker within a single utterance, which can detract from the overall coherence of synthetic speech, particularly in long-form or interactive applications.
Introduction to Speaker Drift
Speaker drift is an underexplored challenge in the realm of TTS, where maintaining a consistent speaker identity is crucial for user engagement and satisfaction. As TTS technologies become more integrated into applications such as virtual assistants, audiobooks, and interactive voice response systems, the implications of speaker drift grow increasingly significant. This article introduces a groundbreaking framework aimed at automatically detecting speaker drift, thus enhancing the reliability of synthesized speech.
Framework Overview
Our proposed framework addresses speaker drift detection by framing it as a binary classification problem that evaluates the consistency of speaker identity at the utterance level. The key components of our approach include:
- Cosine Similarity Computation: We compute the cosine similarity across overlapping segments of synthesized speech, which allows for a nuanced assessment of speaker identity shifts.
- Large Language Models (LLMs): By leveraging structured representations, we prompt LLMs to analyze the computed similarities and assess the presence of drift.
- Theoretical Guarantees: Our method provides theoretical guarantees for the effectiveness of cosine-based drift detection, ensuring a robust framework for practical applications.
Geometric Clustering of Speaker Embeddings
In our analysis, we observe that speaker embeddings exhibit meaningful geometric clustering on the unit sphere. This geometric perspective not only reinforces the validity of our detection framework but also opens new avenues for research in speaker identity and representation in TTS systems.
Benchmark and Evaluation
To evaluate the effectiveness of our speaker drift detection framework, we constructed a high-quality synthetic benchmark that features human-validated speaker drift annotations. This benchmark serves as a critical tool for assessing the performance of various state-of-the-art LLMs in the context of drift detection. Through rigorous experimentation, we confirm the viability of our embedding-to-reasoning pipeline, demonstrating its potential to enhance the coherence of synthesized speech.
Conclusion and Future Work
Our work establishes speaker drift as a distinct and significant research problem within the field of TTS. By bridging geometric signal analysis with LLM-based perceptual reasoning, we provide a novel approach to understanding and mitigating speaker drift in synthesized speech. Future research may explore further refinements of this framework, the integration of additional machine learning techniques, and broader applications across various TTS platforms.
In conclusion, the automatic detection of speaker drift represents a vital step toward achieving more coherent and natural synthesized speech, thereby enhancing user experience in interactive and long-form contexts.