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Found 9 papers, 6 papers with code
Building a Taiwanese Mandarin Spoken Language Model: A First Attempt
This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations.
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
We present Dynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive evaluation of instruction-based universal speech models.
Property Neurons in Self-Supervised Speech Transformers
It is, however, desirable to have an approach that can pinpoint exactly a subset of neurons that is responsible for a particular property of speech, being amenable to model pruning and model editing.
Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models
Speech Integrated Large Language Models (SILLMs) combine large language models with speech perception to perform diverse tasks, such as emotion recognition to speaker verification, demonstrating universal audio understanding capability.
DAISY: Data Adaptive Self-Supervised Early Exit for Speech Representation Models
We introduce Data Adaptive Self-Supervised Early Exit (DAISY), an approach that decides when to exit based on the self-supervised loss, eliminating the need for multiple round of training and fine-tuning.
On the social bias of speech self-supervised models
We probe how various factors, such as model architecture, size, and training methodologies, influence the propagation of social bias within these models.
Compressing Transformer-based self-supervised models for speech processing
Despite the success of Transformers in self- supervised learning with applications to various downstream tasks, the computational cost of training and inference remains a major challenge for applying these models to a wide spectrum of devices.
MelHuBERT: A simplified HuBERT on Mel spectrograms
Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks.
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency.
