2023-05-16 更新
Vocal Style Factorization for Effective Speaker Recognition in Affective Scenarios
Authors:Morgan Sandler, Arun Ross
The accuracy of automated speaker recognition is negatively impacted by change in emotions in a person’s speech. In this paper, we hypothesize that speaker identity is composed of various vocal style factors that may be learned from unlabeled data and re-combined using a neural network architecture to generate holistic speaker identity representations for affective scenarios. In this regard we propose the E-Vector architecture, composed of a 1-D CNN for learning speaker identity features and a vocal style factorization technique for determining vocal styles. Experiments conducted on the MSP-Podcast dataset demonstrate that the proposed architecture improves state-of-the-art speaker recognition accuracy in the affective domain over baseline ECAPA-TDNN speaker recognition models. For instance, the true match rate at a false match rate of 1% improves from 27.6% to 46.2%.
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DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
Authors:Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B., Andreas Maier
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep Filtering (DF) was proposed to directly estimate a complex filter in frequency domain to take advantage of these correlations. In this work, we present a real-time speech enhancement demo using DeepFilterNet. DeepFilterNet’s efficiency is enabled by exploiting domain knowledge of speech production and psychoacoustic perception. Our model is able to match state-of-the-art speech enhancement benchmarks while achieving a real-time-factor of 0.19 on a single threaded notebook CPU. The framework as well as pretrained weights have been published under an open source license.
PDF Accepted as show and tell demo to interspeech 2023
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