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Found 12 papers, 2 papers with code
Investigating the Emergent Audio Classification Ability of ASR Foundation Models
Text and vision foundation models can perform many tasks in a zero-shot setting, a desirable property that enables these systems to be applied in general and low-resource settings.
Towards End-to-End Spoken Grammatical Error Correction
This foundation model can be used to replace the whole framework or part of it, e. g., ASR and disfluency removal.
Zero-shot Audio Topic Reranking using Large Language Models
A key element for this process is highly rapid, flexible, search to support large archives, which in MVSE is facilitated by representing video attributes by embeddings.
Adapting an ASR Foundation Model for Spoken Language Assessment
Additionally, these models have a tendency to skip disfluencies and hesitations in the output.
Can Generative Large Language Models Perform ASR Error Correction?
In this paper we investigate using ChatGPT, a generative LLM, for ASR error correction.
Adapting an Unadaptable ASR System
As speech recognition model sizes and training data requirements grow, it is increasingly common for systems to only be available via APIs from online service providers rather than having direct access to models themselves.
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space
Error correction models form an important part of Automatic Speech Recognition (ASR) post-processing to improve the readability and quality of transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Internal Language Model Estimation based Adaptive Language Model Fusion for Domain Adaptation
The proposed method can achieve significantly better performance on the target test sets while it gets minimal performance degradation on the general test set, compared with both shallow and ILME-based LM fusion methods.
Internal Language Model Estimation Through Explicit Context Vector Learning for Attention-based Encoder-decoder ASR
In this paper we propose two novel approaches to estimate the ILM based on Listen-Attend-Spell (LAS) framework.
An Investigation on Different Underlying Quantization Schemes for Pre-trained Language Models
Recently, pre-trained language models like BERT have shown promising performance on multiple natural language processing tasks.
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
One daunting problem for semantic parsing is the scarcity of annotation.
Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding
The proposed approaches are evaluated on three datasets.
