SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks
Prompt tuning is a technology that tunes a small set of parameters to steer a pre-trained language model (LM) to directly generate the output for downstream tasks. Recently, prompt tuning has demonstrated its storage and computation efficiency in both natural language processing (NLP) and speech processing fields. These advantages have also revealed prompt tuning as a candidate approach to serving pre-trained LM for multiple tasks in a unified manner. For speech processing, SpeechPrompt shows its high parameter efficiency and competitive performance on a few speech classification tasks. However, whether SpeechPrompt is capable of serving a large number of tasks is unanswered. In this work, we propose SpeechPrompt v2, a prompt tuning framework capable of performing a wide variety of speech classification tasks, covering multiple languages and prosody-related tasks. The experiment result shows that SpeechPrompt v2 achieves performance on par with prior works with less than 0.15M trainable parameters in a unified framework.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationLanguage ModelingLanguage ModellingSpoken Language UnderstandingSimilar Papers 제목 키워드 기반
SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech processing tasks. However, utilizing SSL representations usually requires fine-tuning the pre-trained models or designi…
Language ModelingLanguage ModellingSelf-Supervised LearningSpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts
Large language models (LLMs) have gained considerable attention for Artificial Intelligence Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct adaptation of continuous speech to LLM…
Open-Ended Question AnsweringSpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks
Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal training and parameter updates, thus ac…
Language ModelingLanguage ModellingSelf-Supervised LearningPrompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model
Prompting and adapter tuning have emerged as efficient alternatives to fine-tuning (FT) methods. However, existing studies on speech prompting focused on classification tasks and failed on more complex sequence generatio…
Cross-Lingual ASRDecoderslot-fillingSlot FillingUnified Pathological Speech Analysis with Prompt Tuning
Pathological speech analysis has been of interest in the detection of certain diseases like depression and Alzheimer's disease and attracts much interest from researchers. However, previous pathological speech analysis m…
Language ModelingLanguage Modelling