  Large language models have revolutionized natural language processing by
leveraging self-supervised pretraining on vast textual data. Inspired by this
success, researchers have investigated complicated speech tokenization methods
to discretize continuous speech signals so that language modeling techniques
can be applied to speech data. However, existing approaches either model
semantic tokens, potentially losing acoustic information, or model acoustic
tokens, risking the loss of semantic information. Having multiple token types
also complicates the architecture and requires additional pretraining. Here we
show that discretizing mel-filterbank channels into discrete intensity bins
produces a simple representation (dMel), that performs better than other
existing speech tokenization methods. Using a transformer decoder-only
architecture for speech-text modeling, we comprehensively evaluate different
speech tokenization methods on speech recognition (ASR), speech synthesis
(TTS). Our results demonstrate the effectiveness of dMel in achieving high
performance on both tasks within a unified framework, paving the way for
efficient and effective joint modeling of speech and text.
