SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition
Yihan Wu Soumi Maiti Yifan Peng Wangyou Zhang Chenda Li Yuyue Wang Xihua Wang Shinji Watanabe Ruihua Song
Recent advancements in language models have significantly enhanced
performance in multiple speech-related tasks. Existing speech language models
typically utilize task-dependent prompt tokens to unify various speech tasks in
a single model. However, this design omits the intrinsic connections between
different speech tasks, which can potentially boost the performance of each
task. In this work, we propose a novel decoder-only speech language model,
SpeechComposer, that can unify common speech tasks by composing a fixed set of
prompt tokens. Built upon four primary tasks -- speech synthesis, speech
recognition, speech language modeling, and text language modeling --
SpeechComposer can easily extend to more speech tasks via compositions of
well-designed prompt tokens, like voice conversion and speech enhancement. The
unification of prompt tokens also makes it possible for knowledge sharing among
different speech tasks in a more structured manner. Experimental results
demonstrate that our proposed SpeechComposer can improve the performance of
both primary tasks and composite tasks, showing the effectiveness of the shared
prompt tokens. Remarkably, the unified decoder-only model achieves a comparable
and even better performance than the baselines which are expert models designed
for single tasks.