DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition

核心概念

Leveraging entity descriptions improves Named Entity Correction in ASR.

摘要

• Introduction:
  • E2E ASR models face challenges with domain-specific phrases like named entities.
• NEC Approaches:
  • Post-training integration of external language models.
  • Training-time integration of domain-specific contexts.
  • Post-correction on ASR transcripts.
• DANCER Model:
  • Utilizes entity descriptions to mitigate phonetic confusion in NEC.
  • Employs an EDA-MLM for efficient adaptation to domain-specific entities.
• Experiments:
  • Outperforms PED-NEC in CER reduction for named entities.
  • Shows significant improvement on Homophone dataset.
• Methodology:
  • Corrupted Entity Detection.
  • Phonetic-level Retrieval.
  • Entity Description Augmented MLM.
• Results:
  • Few-shot generalization ability.
  • Impact of entity list size.
  • Impact of different settings of alpha and top-k.

統計資料

DANCER outperforms PED-NEC by a CER reduction of about 7% on AISHELL-1.
DANCER offers a more pronounced CER reduction of 46% on Homophone dataset.
NE list contained 16,168 distinct named entities.

引述

"Our approach leads to a better reduction in CER for both datasets."
"Incorporating the entity rejection mechanism may slightly decrease the NE recall rate."

從以下內容提煉的關鍵洞見

DANCER

by Yi-Cheng Wan... 於 arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17645.pdf

深入探究

How can incorporating entity descriptions improve ASR beyond named entity correction?

Incorporating entity descriptions can improve ASR beyond named entity correction by providing additional semantic context to the model. This additional information helps the ASR system better understand the meaning and context of the named entities, leading to more accurate transcriptions. By leveraging entity descriptions, the ASR system can enhance its overall understanding of the content being spoken, leading to improved transcription quality, especially in cases where named entities are crucial for understanding the context of the speech.

What are the limitations of relying on entity descriptions for phonetic confusion mitigation?

While relying on entity descriptions for phonetic confusion mitigation can be effective, there are some limitations to consider. One limitation is the availability and quality of entity descriptions. If the descriptions are not comprehensive or accurate, they may not provide the necessary information to mitigate phonetic confusion effectively. Additionally, the process of curating and maintaining entity descriptions can be time-consuming and resource-intensive. Moreover, in cases where the entity descriptions do not align well with the spoken language or dialect, they may not effectively address phonetic confusion, leading to transcription errors.

How can the DANCER model be adapted for other languages or speech recognition systems?

The DANCER model can be adapted for other languages or speech recognition systems by following a few key steps:

Data Collection: Gather a dataset in the target language with named entities and their corresponding descriptions.
Entity Description Construction: Create entity-description pairs in the target language using relevant sources such as local encyclopedias or databases.
Model Training: Train the DANCER model on the new dataset, ensuring that the model can effectively leverage entity descriptions for phonetic confusion mitigation.
Fine-tuning: Fine-tune the model on the specific characteristics and nuances of the target language to optimize performance.
Evaluation and Iteration: Evaluate the model on test data in the new language, analyze performance, and iterate on the model to improve accuracy and efficiency in the new language or speech recognition system.