Communication has been an essential part of being human and living in society. There are
several different languages and variations of them, so you can speak English in one place …

Training a conventional automatic speech recognition (ASR) system to support multiple
languages is challenging because the sub-word unit, lexicon and word inventories are …

Developing artificial learning systems that can understand and generate natural language
has been one of the long-standing goals of artificial intelligence. Recent decades have …

Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new
direction in speech research. The approach benefits by performing model training without …

In this paper, we proposed to apply meta learning approach for low-resource automatic
speech recognition (ASR). We formulated ASR for different languages as different tasks, and …

Automatic spoken language identification (LID) is a very important research field in the era of
multilingual voice-command-based human-computer interaction. A front-end LID module …

Techniques for multi-lingual and cross-lingual speech recognition can help in low resource
scenarios, to bootstrap systems and enable analysis of new languages and domains. End-to …

In this work, we propose a new parameter-efficient learning framework based on neural
model reprogramming for cross-lingual speech recognition, which can re-purpose well …

Code-switching (CS) is a common phenomenon and recognizing CS speech is challenging.
But CS speech data is scarce and there's no common testbed in relevant research. This …

Transfer learning from a multilingual model has shown favorable results on low-resource
automatic speech recognition (ASR). However, full-model fine-tuning generates a separate …