Search Results for author:
Found 12 papers, 5 papers with code
A Preliminary Exploration with GPT-4o Voice Mode
Additionally, GPT-4o's safety mechanisms cause it to decline tasks like speaker identification, age classification, MOS prediction, and audio deepfake detection.
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.
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.
Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement
In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction.
Neural Codec-based Adversarial Sample Detection for Speaker Verification
In this paper, we propose a neural codec-based adversarial sample detection method for ASV.
Singing Voice Graph Modeling for SingFake Detection
Detecting singing voice deepfakes, or SingFake, involves determining the authenticity and copyright of a singing voice.
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.
Towards audio language modeling - an overview
Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency.
Multimodal Transformer Distillation for Audio-Visual Synchronization
This paper proposed an MTDVocaLiST model, which is trained by our proposed multimodal Transformer distillation (MTD) loss.
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection
Audio-visual active speaker detection (AVASD) is well-developed, and now is an indispensable front-end for several multi-modal applications.
Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification
The countermeasure (CM) model is developed to protect ASV systems from spoof attacks and prevent resulting personal information leakage in Automatic Speaker Verification (ASV) system.
