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Found 8 papers, 1 papers with code
Can GPT-4 do L2 analytic assessment?
Automated essay scoring (AES) to evaluate second language (L2) proficiency has been a firmly established technology used in educational contexts for decades.
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.
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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L2 proficiency assessment using self-supervised speech representations
Though the wav2vec 2. 0 based system is found to be sensitive to the nature of the response, it can be configured to yield comparable performance to systems requiring a speech transcription, and yields gains when appropriately combined with standard approaches.
