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Found 7 papers, 0 papers with code
Speaker-conditioned Target Speaker Extraction based on Customized LSTM Cells
In this paper, we focus on a single-channel target speaker extraction system based on a CNN-LSTM separator network and a speaker embedder network requiring reference speech of the target speaker.
Training Strategies for Own Voice Reconstruction in Hearing Protection Devices using an In-ear Microphone
In this paper, we apply a deep learning-based bandwidth-extension system to the own voice reconstruction task and investigate different training strategies in order to overcome the limited availability of training data.
Speaker-conditioning Single-channel Target Speaker Extraction using Conformer-based Architectures
Target speaker extraction aims at extracting the target speaker from a mixture of multiple speakers exploiting auxiliary information about the target speaker.
Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping
Our system achieves a promising accuracy for streaming keyword spotting and keyword search on Common Voice audio using just 5 examples per keyword.
Speech-dependent Modeling of Own Voice Transfer Characteristics for In-ear Microphones in Hearables
To enhance the quality of the in-ear microphone signal using algorithms aiming at joint bandwidth extension, equalization, and noise reduction, it is desirable to have an accurate model of the own voice transfer characteristics between the entrance of the ear canal and the in-ear microphone.
Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones
In this paper, we propose a speech-dependent model of the own voice transfer characteristics based on phoneme recognition, assuming a linear time-invariant relative transfer function for each phoneme.
Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments
Recording a sufficient amount of noise required for training such a system is costly since noise transmission between outer and inner microphones varies individually.
