  Recent advancements in generative speech models based on audio-text prompts
have enabled remarkable innovations like high-quality zero-shot text-to-speech.
However, existing models still face limitations in handling diverse audio-text
speech generation tasks involving transforming input speech and processing
audio captured in adverse acoustic conditions. This paper introduces SpeechX, a
versatile speech generation model capable of zero-shot TTS and various speech
transformation tasks, dealing with both clean and noisy signals. SpeechX
combines neural codec language modeling with multi-task learning using
task-dependent prompting, enabling unified and extensible modeling and
providing a consistent way for leveraging textual input in speech enhancement
and transformation tasks. Experimental results show SpeechX's efficacy in
various tasks, including zero-shot TTS, noise suppression, target speaker
extraction, speech removal, and speech editing with or without background
noise, achieving comparable or superior performance to specialized models
across tasks. See https://aka.ms/speechx for demo samples.
