Speechless: Speech Instruction Training Without Speech for Low Resource
  Languages
Authors: name: Alan Dao, name: Dinh Bach Vu, name: Huy Hoang Ha, name: Tuan Le Duc Anh, name: Shreyas Gopal, name: Yue Heng Yeo, name: Warren Keng Hoong Low, name: Eng Siong Chng, name: Jia Qi Yip

The rapid growth of voice assistants powered by large language models (LLM)
has highlighted a need for speech instruction data to train these systems.
Despite the abundance of speech recognition data, there is a notable scarcity
of speech instruction data, which is essential for fine-tuning models to
understand and execute spoken commands. Generating high-quality synthetic
speech requires a good text-to-speech (TTS) model, which may not be available
to low resource languages. Our novel approach addresses this challenge by
halting synthesis at the semantic representation level, bypassing the need for
TTS. We achieve this by aligning synthetic semantic representations with the
pre-trained Whisper encoder, enabling an LLM to be fine-tuned on text
instructions while maintaining the ability to understand spoken instructions
during inference. This simplified training process is a promising approach to
building voice assistant for low-resource languages.