Tootfinder

A Comparative Analysis on ASR System Combination for Attention, CTC, Factored Hybrid, and Transducer Models
Noureldin Bayoumi, Robin Schmitt, Tina Raissi, Albert Zeyer, Ralf Schl\"uter, Hermann Ney
https://arxiv.org/abs/2508.09880

A Comparative Analysis on ASR System Combination for Attention, CTC, Factored Hybrid, and Transducer Models
Combination approaches for speech recognition (ASR) systems cover structured sentence-level or word-based merging techniques as well as combination of model scores during beam search. In this work, we compare model combination across popular ASR architectures. Our method leverages the complementary strengths of different models in exploring diverse portions of the search space. We rescore a joint hypothesis list of two model candidates. We then identify the best hypothesis through log-linear co…

RAG-Boost: Retrieval-Augmented Generation Enhanced LLM-based Speech Recognition
Pengcheng Wang, Sheng Li, Takahiro Shinozaki
https://arxiv.org/abs/2508.14048 https://

RAG-Boost: Retrieval-Augmented Generation Enhanced LLM-based Speech Recognition
In this paper, we propose RAG-Boost (ST-ShinozakiLab Task I system), which enhances the baseline LLM-based ASR system of the MLC-SLM Challenge (task I) with a retrieval-augmented generation (RAG) module on the fly. Each partial ASR hypothesis queries a vector store of audio-text pairs and domain terms, and the retrieved results are fused with the live ASR hypotheses to fix recognition errors. The fused hypotheses are passed to the LLM, yielding improved responses.

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Flash floods sweep cars away in Saudi Arabia’s Asir
Kingdom warns of heavy rains, flash floods across 10 regions

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…

Testing for LLM response differences: the case of a composite null consisting of semantically irrelevant query perturbations
Aranyak Acharyya, Carey E. Priebe, Hayden S. Helm
https://arxiv.org/abs/2509.10963

Testing for LLM response differences: the case of a composite null consisting of semantically irrelevant query perturbations
Given an input query, generative models such as large language models produce a random response drawn from a response distribution. Given two input queries, it is natural to ask if their response distributions are the same. While traditional statistical hypothesis testing is designed to address this question, the response distribution induced by an input query is often sensitive to semantically irrelevant perturbations to the query, so much so that a traditional test of equality might indicate …

Objective and Subjective Evaluation of Diffusion-Based Speech Enhancement for Dysarthric Speech
Dimme de Groot, Tanvina Patel, Devendra Kayande, Odette Scharenborg, Zhengjun Yue
https://arxiv.org/abs/2508.17980

Objective and Subjective Evaluation of Diffusion-Based Speech Enhancement for Dysarthric Speech
Dysarthric speech poses significant challenges for automatic speech recognition (ASR) systems due to its high variability and reduced intelligibility. In this work we explore the use of diffusion models for dysarthric speech enhancement, which is based on the hypothesis that using diffusion-based speech enhancement moves the distribution of dysarthric speech closer to that of typical speech, which could potentially improve dysarthric speech recognition performance. We assess the effect of two d…