{"id":962034,"date":"2023-08-13T17:10:42","date_gmt":"2023-08-14T00:10:42","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=962034"},"modified":"2023-08-13T18:03:40","modified_gmt":"2023-08-14T01:03:40","slug":"speechx-neural-codec-language-model-as-a-versatile-speech-transformer","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/speechx-neural-codec-language-model-as-a-versatile-speech-transformer\/","title":{"rendered":"SpeechX: Neural Codec Language Model as a Versatile Speech Transformer"},"content":{"rendered":"

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 (opens in new tab)<\/span><\/a> for demo samples.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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