COALA: Robust Contextualized Speech-augmented Language Modeling for ASR via Contrastive Regularizer and Biasing Score Estimation
arXiv:2607.08117v1 Announce Type: new Abstract: Contextual biasing seeks to integrate external knowledge into automatic speech recognition (ASR) systems to accurately recognize domain-specific entities. In this paper, we propose COALA (Contextualized ASR Leveraging Biasing Scoring), a robust framework designed to enhance speech-augmented language models (SLMs) in complex multi-entity scenarios. Considering the inherent context-window limitations of SLMs, identifying relevant target entities from a large-scale biasing list is crucial for effective recognition. To this end, COALA maps SLM latent
The continuous drive to improve the accuracy and contextual understanding of AI-driven speech recognition systems necessitates ongoing research into more robust language models.
Improved speech recognition, especially in complex, domain-specific contexts, enhances human-computer interaction and expands the utility of AI in various sectors.
This advancement proposes a framework that could lead to significantly more accurate and contextually aware ASR systems, particularly in scenarios with numerous potential entities.
- · ASR providers
- · Voice assistant developers
- · Call center technology
- · Domain-specific AI applications
- · Legacy ASR systems
- · Companies reliant on less accurate transcription
ASR systems become more reliable and versatile, reducing errors in transcriptions and voice commands.
Increased adoption of voice interfaces in complex professional settings due to enhanced accuracy and contextual understanding.
New forms of data analysis and business intelligence emerge from more accurate and detailed speech-to-text conversion across industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL