PhonoVoxtLM: Exploring Single-Codebook Phonological Tokens for Speech Understanding and Generation

Abstract

We investigate how speech token representations can support both understanding and generation within a unified speech language model. We present PhonoVoxtLM, built on the existing VoxtLM framework using phonological tokens designed to retain linguistic content and prosodic information. The existing tokenizer is jointly trained for speech recognition and resynthesis. Its single-codebook units serve as both speech inputs and generation targets, without additional speech-token prediction modules. Experiments show improvements over HuBERT-based baselines across understanding and generation under different training-data scales. Further comparisons with WavLM-based tokens reveal trade-offs between recognition and synthesis, highlighting the importance of evaluating shared representations across both capabilities. We further extend the framework to spoken question answering through fine-tuning with replay. The adapted model improves answering performance over similarly adapted HuBERT baselines while retaining advantages on the original speech tasks. Our findings support single-codebook representations designed for both content and prosody as a practical choice for unified multitask speech modeling.