Tootfinder

Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition
Chin Yuen Kwok, Jia Qi yip
https://arxiv.org/abs/2509.09196 https://arxiv.or…

Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition
Contextual biasing improves rare word recognition of ASR models by prioritizing the output of rare words during decoding. A common approach is Trie-based biasing, which gives "bonus scores" to partial hypothesis (e.g. "Bon") that may lead to the generation of the rare word (e.g. "Bonham"). If the full word ("Bonham") isn't ultimately recognized, the system revokes those earlier bonuses. This revocation is limited to beam search and is computationally expensive, particularly for models with larg…

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function
Chin Yuen Kwok, Jia Qi Yip, Eng Siong Chng
https://arxiv.org/abs/2509.09197 ht…

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function
Rare word recognition can be improved by adapting ASR models to synthetic data that includes these words. Further improvements can be achieved through contextual biasing, which trains and adds a biasing module into the model architecture to prioritize rare words. While training the module on synthetic rare word data is more effective than using non-rare-word data, it can lead to overfitting due to artifacts in the synthetic audio. To address this, we enhance the TCPGen-based contextual biasing …

Unlocking the Potential of Soft Actor-Critic for Imitation Learning
Nayari Marie Lessa, Melya Boukheddimi, Frank Kirchner
https://arxiv.org/abs/2509.24539 https://

Unlocking the Potential of Soft Actor-Critic for Imitation Learning
Learning-based methods have enabled robots to acquire bio-inspired movements with increasing levels of naturalness and adaptability. Among these, Imitation Learning (IL) has proven effective in transferring complex motion patterns from animals to robotic systems. However, current state-of-the-art frameworks predominantly rely on Proximal Policy Optimization (PPO), an on-policy algorithm that prioritizes stability over sample efficiency and policy generalization. This paper proposes a novel IL f…

NATO-Militärchefs beraten über Ukraine
Die Militärchefs der NATO-Staaten haben dem Vorsitzenden des Militärausschusses zufolge eine großartige und offene Diskussion über die Lage in der Ukraine geführt. "In Bezug auf die Ukraine haben wir unsere Unterstützung bestätigt. Priorität hat weiterhin ein gerechter, glaubwürdiger und dauerhafter Frieden", schrieb Admiral Giuseppe Cavo Dragone nach einer Videokonferenz auf X.
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Ukraine-Liveblog: ++ Litauen bietet Truppen zur Friedenssicherung an ++
Litauen würde Soldaten und Ausrüstung zu einer möglichen Friedensmission in die Ukraine schicken - das sagte Präsident Nauseda in einem Interview. In der Ostukraine sind durch russischen Beschuss offenbar drei Menschen getötet worden.