Title: CLARA: Convex Low-resource Accent-Robust Language Detection in ASR
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Convex Neural Networks, ASR, Spoken Dialogue Systems, Low-resource Languages

Abstract: Globalization and multiculturalism have produced diverse speech varieties (e.g., Singaporean-accented English and regional Hindi dialects) that remain underrepresented even in high-resource language data. As a result, spoken dialogue systems frequently misidentify the user’s input language up to 49.33\% of the time, degrading response accuracy regardless of language model capability. 
We propose a robust ASR framework for handling low-resource dialectal variance with reduced computational overhead, and lightweight training costs. Our Convex Language Detection (CLD) algorithm integrates a convex reformulation of NN, and is solved efficiently with ADMM in JAX. This provides human-level sub-500ms inference latency, strong convergence guarantees and reduced sample complexity. As a motivating case study, CLD significantly improves transcription accuracy on mixed-dialect inputs when integrated with Whisper encoders. This demonstrates promising directions for principled statistical generalization in spoken dialogue systems for low-resource languages.

TL;DR: Using convex neural networks to improve ASR transcription accuracy in low-resource dialectical spoken input.