dr Grazina Korvel
- SPEECH RECOGNITION, ALLOPHONE, PHONOLOGY, FOREIGN LANGUAGE, AUDIO FEATURES
- 2D SPACE FEATURE, SPEECH ANALYSIS, DEEP LEARNING, SPECTROGRAM, CEPSTROGRAM, CHROMAGRAM
- ALGORYTM KNN
- ANALIZA FONEMATYCZNA
- AUTOMATYCZNE ROZPOZNAWANIE MOWY, SPLOTOWE SIECI, GŁĘBOKIE UCZENIE, CHROMAGRAMY, WYMIAR FRAKTALNY
- CONVOLUTIONAL NEURAL NETWORKS
- DATA PREPARATION
- DEEP LEARNING
- EFEKT LOMBARDA, ANALIZA SYGNAŁU MOWY
- FACIAL MOTION CAPTURE
A voiceless stop consonant phoneme modelling and synthesis framework based on a phoneme modelling in low-frequency range and high-frequency range separately is proposed. The phoneme signal is decomposed into the sums of simpler basic components and described as the output of a linear multiple-input and single-output (MISO) system. The impulse response of each channel is a third order quasi-polynomial. Using this framework, the...
In this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information...
Analysis of Lombard speech using parameterization and the objective quality indicators in noise conditions
The aim of the work is to analyze Lombard speech effect in recordings and then modify the speech signal in order to obtain an increase in the improvement of objective speech quality indicators after mixing the useful signal with noise or with an interfering signal. The modifications made to the signal are based on the characteristics of the Lombard speech, and in particular on the effect of increasing the fundamental frequency...
wyświetlono 12 razy