This paper describes a hands-free speech recognition method based on HMM composition and separation for speech contaminated not only by additive noise but also by an acoustic transfer function. The method re­ alizes an improved user interface such that a user is not encumbered by microphone equipment in noisy and re­ verberant environments. In this approach， an attempt is made to model acoustic...

In this article, a novel voice activity detection (VAD) approach based on phoneme recognition using Gaussian Mixture Model based Hidden Markov Model (HMM/GMM) is proposed. Some sophisticated speech features such as high order statistics (HOS), harmonic structure information and Mel-frequency cepstral coefficients (MFCCs) are employed to represent each speech/non-speech segment. The main idea of...

This paper assesses the ability of a HMM-based speech synthesis systems to model the speech characteristics of various speaking styles. A discrete/continuous HMM is presented to model the symbolic and acoustic speech characteristics of a speaking style. The proposed model is used to model the average characteristics of a speaking style that is shared among various speakers, depending on specifi...

In this paper, an iterative bandwidth mismatch compensation (BMC) algorithm is proposed to alleviate the need of multiple pre-trained models for recognizing different bandwidth speech. The BMC uses the concept of the bandwidth extension as similar as in the speech enhancement approaches. However, it aims at directly improving the recognition accuracy instead of speech intelligence or quality an...

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 ...