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Found 6 papers, 2 papers with code
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling.
SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks
Motivated by the strengths of prompting, we are the first to explore the potential of prompting speech LMs in the domain of speech processing.
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models
The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance.
SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts
In this paper, we present pioneering research that explores the application of prompt tuning to stimulate speech LMs for various generation tasks, within a unified framework called SpeechGen, with around 10M trainable parameters.
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting
User-defined keyword spotting is a task to detect new spoken terms defined by users.
MITAS: A Compressed Time-Domain Audio Separation Network with Parameter Sharing
Our experimental results first confirmed the robustness of our MiTAS on two types of perturbations in mixed audio.
