REVIEW 2 cited by
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMs
LiSTEN shows that dynamically selecting a few learnable prompt tokens from a shared pool can replace LoRA fine-tuning for audio-language models, matching or beating it with less training data.
-
TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation
Adding task-specific learned vectors to acoustic embeddings lets a transducer ASR model train on partially labeled data, matching or beating the fully labeled TokenVerse baseline on most tasks.
Discussion (0). Continue with ORCID to comment.