Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis
نویسندگان
چکیده
منابع مشابه
A Comparative Study of Gender and Age Classification in Speech Signals
Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in a...
متن کاملProbabilistic Speaker-Class based Acoustic Modeling for Large Vocabulary Continuous Speech Recognition
In this paper, a probabilistic speaker-class (PSC) based acoustic modeling method is proposed for taking into account speaker variability influence in HMM-based speech recognition systems. Firstly, within the context of speaker-class based speech recognition, an experiment is conducted to investigate the performance of speaker-class recognition based on hard-cut speaker clustering. Then, in the...
متن کاملA probabilistic trajectory synthesis system for synthesising visual speech
We describe an unsupervised probabilistic approach for synthesising visual speech from audio. Acoustic features representing a training corpus are clustered and the probability density function (PDF) of each cluster is modelled as a Gaussian mixture model (GMM). A visual target in the form of a shortterm parameter trajectory is generated for each cluster. Synthesis involves combining the cluste...
متن کاملGenerative Acoustic-Phonemic-Speaker Model Based on Three-Way Restricted Boltzmann Machine
In this paper, we argue the way of modeling speech signals based on three-way restricted Boltzmann machine (3WRBM) for separating phonetic-related information and speaker-related information from an observed signal automatically. The proposed model is an energy-based probabilistic model that includes three-way potentials of three variables: acoustic features, latent phonetic features, and speak...
متن کاملProbabilistic latent speaker training for large vocabulary speech recognition
In this paper, we describe an improvement on probabilistic latent speaker analysis method and investigate the use of probabilistic latent speaker analysis for acoustic model training. By performing co-occurrence analysis between speaker and dominant components, speaker variation is dealt with based on different trajectories. Speech recognition experiment results show that our method, although w...
متن کامل