arXiv:2606.25460v1 Announce Type: cross Abstract: Recent advances in sequence modeling have significantly improved ASR systems, bringing them close to human-level recognition accuracy and enhancing robustness across diverse acoustic conditions and languages. In contrast, Forced Alignment has not experienced comparable progress, and traditional HMM-GMM frameworks remain widely adopted and highly competitive. To address this gap, we propose an end-to-end, fully differentiable neural architecture specifically designed for phoneme alignment. The model consists of an encoder that processes the inpu
The continuous advancements in sequence modeling and neural networks are enabling researchers to apply modern AI techniques to long-standing problems like forced alignment, which previously relied on older statistical models.
This development indicates a potential modernization of foundational speech technology, opening avenues for more accurate and robust language processing applications that could impact a wide range of AI systems.
Traditional HMM-GMM frameworks for forced alignment may begin to be replaced by fully differentiable neural architectures, leading to improved performance and potentially more integrated end-to-end AI systems.
- · AI researchers
- · Speech technology developers
- · ASR system providers
- · Language learning platforms
- · Legacy HMM-GMM system providers
Improved phoneme alignment accuracy will lead to better performance in various speech processing applications.
More robust and accurate speech technologies could enhance the capabilities of AI agents that rely on voice interaction and analysis.
Enhanced understanding of speech nuances might enable new forms of human-computer interaction and personalized AI experiences.
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Read at arXiv cs.CL