Search Results for author:
Found 7 papers, 2 papers with code
Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations
Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i. e., data that can be stored in a matrix.
ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models
First, we provide an open-source platform for researchers in the speaker recognition community to effortlessly build models.
Ranked #1 on
Speaker Verification
on VoxCeleb
(using extra training data)
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study
Our investigation reveals that SE can improve KWS accuracy on noisy speech when the backend model is trained on clean speech; however, despite our extensive exploration, it is difficult to improve the KWS accuracy with SE when the backend is trained on noisy speech.
Multichannel Voice Trigger Detection Based on Transform-average-concatenate
Voice triggering (VT) enables users to activate their devices by just speaking a trigger phrase.
Improving Voice Trigger Detection with Metric Learning
A detector is typically trained on speech data independent of speaker information and used for the voice trigger detection task.
Multi-task Learning with Cross Attention for Keyword Spotting
In this approach, an output of an acoustic model is split into two branches for the two tasks, one for phoneme transcription trained with the ASR data and one for keyword classification trained with the KWS data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Dynamic curriculum learning via data parameters for noise robust keyword spotting
We propose dynamic curriculum learning via data parameters for noise robust keyword spotting.
