Scaling Up Multilingual Automatic Speech Recognition: Insights from an Industrial-Grade System
This paper describes the development of an industrial-scale, multilingual automatic speech recognition (ASR) system that leverages a diverse training dataset and a robust model architecture to achieve competitive performance and practical advantages over state-of-the-art open-source models.
Expanding Automatic Speech Recognition to New Languages: Strategies for Integrating Low-Resource Languages into Foundation Models
Efficiently integrating new low-resource languages into a pre-trained multilingual automatic speech recognition (ASR) foundation model while maintaining performance on existing languages.
Configurable Multilingual Automatic Speech Recognition Using Speech Summary Representations
This research paper introduces csvMASR, a novel configurable multilingual automatic speech recognition (MASR) model that leverages speech summary vector representations and adapter modules to achieve improved performance and configurability compared to existing MASR models.
Improving Multilingual ASR Performance Using a Two-Stage Transliteration Approach
A two-stage transliteration approach, projecting graphemes from multiple languages to a common script (Devanagari), significantly improves the performance of end-to-end multilingual Automatic Speech Recognition (ASR) systems by reducing speech-class confusion.
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