Speaker Identification
61 papers with code • 4 benchmarks • 4 datasets
Most implemented papers
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer.
Generative Pre-Training for Speech with Autoregressive Predictive Coding
Learning meaningful and general representations from unannotated speech that are applicable to a wide range of tasks remains challenging.
Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models
We compare the three best architectures trained using our method to select the best one, which is the one with a shallow architecture.
Contrastive Learning of General-Purpose Audio Representations
We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio.
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
Speaker identification models are vulnerable to carefully designed adversarial perturbations of their input signals that induce misclassification.
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations
We propose using self-supervised discrete representations for the task of speech resynthesis.
SSAST: Self-Supervised Audio Spectrogram Transformer
However, pure Transformer models tend to require more training data compared to CNNs, and the success of the AST relies on supervised pretraining that requires a large amount of labeled data and a complex training pipeline, thus limiting the practical usage of AST.
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
PaddleSpeech is an open-source all-in-one speech toolkit.
A Generative Product-of-Filters Model of Audio
We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain.
A domain-agnostic approach for opinion prediction on speech
We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction.