R. Schluter

Gradient-based alignment can outperform traditional methods in challenging scenarios, offering a universal solution for precise speech-to-text mapping across diverse ASR models.

Large output values from positional encodings can severely compromise the performance of memristor-based computations, but targeted adjustments can halve this degradation.

Diffusion language models can substantially boost speech recognition accuracy, rivaling traditional language models while offering unique advantages like bidirectional attention.

Forget simply bolting on an LLM: this work reveals the surprisingly intricate dance between acoustic models and LLMs needed to unlock state-of-the-art speech recognition.