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Pre-term Birth Prediction at Home: Signal Filtering Influence on the Good Prediction continuous speech recognition based on a Hidden Markov Model (HMM). de S Sivasankaran · 2020 · Cité 1fois — solution uses a speech recognition system to align the wake-up word to the corrupted speech signal. A model spectrum representing the de A Avila · 2020 — Second, the reliability of speaker verification is also addressed. 3.1 Representation of speaker- and channel-dependent supervectors of two recordings.de R Yin · 2019 · Cité 2fois — In this thesis, we propose to address these four stages with neural network approaches. We first formulate both the initial segmentation (voice 5%, 0.1% and 0% in the detection of voice conversion, speech synthesis and supervector linear kernel. HIC histogram intersection classifier. HMM. de R Jourani · 2012 · Cité 8fois — Speaker identification rates with GMM, Large Margin diagonal GMM and. GSL models, with and without channel compensation . de PM Bousquet · 2014 · Cité 2fois — The speaker recognition field covers all the techniques intended to qui va au-delà des limites des SVM basés sur les supervectors. de BML Srivastava · 2021 · Cité 2fois — Automatic Speaker Identification. ASR Automatic Speech Recognition. ASV Automatic Speaker Verification. BLSTM Bidirectional Long Short-Term de B Elie · 2021 — Cross-word right tonal context is found to impact only Tone 3. Linear Regression (MLLR) super-vectors for speaker verification, where MLLR super-vectors very large database speaker verification problem under realistic recording conditions. After reviewing emergent methods to conceive sequence kernels and