Fully Differentiable Neural Forced Alignment via Soft Dynamic Programming
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 input signal and a decoder that produces alignment decisions. The encoder is structured into two complementary branches: one dedicated to phoneme ident...
| Authors | Rotem Rousso, Eyal Cohen, Joseph Keshet |
|---|---|
| Primary category | eess.AS |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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