Search Results for author:
Found 11 papers, 4 papers with code
Recent Advances in Discrete Speech Tokens: A Review
The rapid advancement of speech generation technologies in the era of large language models (LLMs) has established discrete speech tokens as a foundational paradigm for speech representation.
Why Do Speech Language Models Fail to Generate Semantically Coherent Outputs? A Modality Evolving Perspective
Although text-based large language models exhibit human-level writing ability and remarkable intelligence, speech language models (SLMs) still struggle to generate semantically coherent outputs.
LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec
Voice conversion evaluations prove the satisfactory speaker disentanglement of LSCodec, and ablation study further verifies the effectiveness of the proposed training framework.
vec2wav 2.0: Advancing Voice Conversion via Discrete Token Vocoders
To amend the loss of speaker timbre in the content tokens, vec2wav 2. 0 utilizes the WavLM features to provide strong timbre-dependent information.
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data.
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system.
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance
Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data.
Feature Space Singularity for Out-of-Distribution Detection
Our analysis of the phenomenon reveals why our algorithm works.
Ranked #1 on
Out-of-Distribution Detection
on MS-1M vs. IJB-C
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Shallow VAEs with RealNVP Prior Can Perform as Well as Deep Hierarchical VAEs
Using powerful posterior distributions is a popular technique in variational inference.
On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
Using powerful posterior distributions is a popular approach to achieving better variational inference.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.
