Acoustic-to-articulatory inverse mapping using an HMM-based speech production model
نویسندگان
چکیده
We present a method that determines articulatory movements from speech acoustics using an HMM (Hidden Markov Model)-based speech production model. The model statistically generates speech acoustics and articulatory movements from a given phonemic string. It consists of HMMs of articulatory movements for each phoneme and an articulatory-to-acoustic mapping for each HMM state. For a given speech acoustics, the maximum a posteriori probability estimate of the articulatory parameters of the statistical model is presented. The method’s performance on sentences was evaluated by comparing the estimated articulatory parameters with observed parameters. The average rms error of the estimated articulatory parameters was 1.79 mm with phonemic information and 2.16 mm without phonemic information in an utterance.
منابع مشابه
Acoustic-to-articulatory inversion using a speaker-normalized HMM-based speech production model
Acoustic-to-articulatory inverse mapping is a difficult problem because of its non-linear and oneto-many characteristics. We have previously developed a speech inversion method using a hidden Markov model (HMM)-based speech production model which takes into account the phonemespecific dynamic constraints of articulatory parameters. We found that the constraint significantly decreases the estima...
متن کاملIntegrating Articulatory Information in Deep Learning-Based Text-to-Speech Synthesis
Articulatory information has been shown to be effective in improving the performance of hidden Markov model (HMM)based text-to-speech (TTS) synthesis. Recently, deep learningbased TTS has outperformed HMM-based approaches. However, articulatory information has rarely been integrated in deep learning-based TTS. This paper investigated the effectiveness of integrating articulatory movement data t...
متن کاملContinuous Articulatory-to-Acoustic Mapping using Phone-based Trajectory HMM for a Silent Speech Interface
The article presents an HMM-based mapping approach for converting ultrasound and video images of the vocal tract into an audible speech signal, for a silent speech interface application. The proposed technique is based on the joint modeling of articulatory and spectral features, for each phonetic class, using Hidden Markov Models (HMM) and multivariate Gaussian distributions with full covarianc...
متن کاملIntegration of articulatory and spectrum features based on the hybrid HMM/BN modeling framework
Most of the current state-of-the-art speech recognition systems are based on speech signal parametrizations that crudely model the behavior of the human auditory system. However, little or no use is usually made of the knowledge on the human speech production system. A data-driven statistical approach to incorporate this knowledge into ASR would require a substantial amount of data, which are n...
متن کاملGeneralized variable parameter HMMs based acoustic-to-articulatory inversion
Acoustic-to-articulatory inversion is useful for a range of related research areas including language learning, speech production, speech coding, speech recognition and speech synthesis. HMM-based generative modelling methods and DNNbased approaches have become dominant approaches in recent years. In this paper, a novel acoustic-to-articulatory inversion technique based on generalized variable ...
متن کامل