Search Results for author:
Found 7 papers, 1 papers with code
openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer
Household speaker identification with few enrollment utterances is an important yet challenging problem, especially when household members share similar voice characteristics and room acoustics.
Improving fairness in speaker verification via Group-adapted Fusion Network
This is observed especially with underrepresented demographic groups sharing similar voice characteristics.
Contrastive-mixup learning for improved speaker verification
In this work, we propose contrastive-mixup, a novel augmentation strategy that learns distinguishing representations based on a distance metric.
ASR-Aware End-to-end Neural Diarization
We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets
Second, a scoring function is applied between a runtime utterance and each speaker profile.
End-to-end Neural Diarization: From Transformer to Conformer
We propose a new end-to-end neural diarization (EEND) system that is based on Conformer, a recently proposed neural architecture that combines convolutional mappings and Transformer to model both local and global dependencies in speech.
BW-EDA-EEND: Streaming End-to-End Neural Speaker Diarization for a Variable Number of Speakers
We present a novel online end-to-end neural diarization system, BW-EDA-EEND, that processes data incrementally for a variable number of speakers.
