A profile hidden Markov model (PHMM) is widely used in assigning protein sequences to protein families. In this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. In other words, in the PHMM, only the information of the left side of a hidden state is considered. However, it makes sense that considering the information of the b...

In previous work, we have proposed a method to control the characteristics of synthetic speech flexibly by integrating articulatory features into hidden Markov model (HMM) based parametric speech synthesis. A unified acoustic-articulatory model was trained and a piecewise linear transform was adopted to describe the dependency between these two feature streams. The transform matrices were train...

This work proposes a method to predict the fundamental frequency and voicing of a frame of speech from its MFCC representation. This has particular use in distributed speech recognition systems where the ability to predict fundamental frequency and voicing allows a time-domain speech signal to be reconstructed solely from the MFCC vectors. Prediction is achieved by modeling the joint density of...

This paper identifies various concepts involved in social networks for finding the emerging topics. We focus on the various methods that can be applied for detecting the anomaly. The methods used are Hidden Markov Model, UMass Approach, CMU Approach, Change Finder method and Finite Mixture Model. These methods involve texts, videos, audios, URLs and mentions which are shared in the social netwo...

This paper presents a novel training algorithm for Hidden Markov Model (HMM)-based speech synthesis. One of the biggest issues causing significant quality degradation in synthetic speech is the over-smoothing effect often observed in generated speech parameter trajectories. Recently, we have found that a Modulation Spectrum (MS) of the generated speech parameters is sensitively correlated with ...

Laryngectomee patients lose their ability to produce speech sounds and suffer in their daily communication. There are currently limited communication options for these patients. Silent speech interfaces (SSIs), which recognize speech from articulatory information (i.e., without using audio information), have potential to assist the oral communication of persons with laryngectomy or other speech...

Recent research suggests that modeling coarticulation in speech is more appropriate at the syllable level. However, due to a number of additional factors that can affect the way syllables are articulated, creating multiple acoustic models per syllable might be necessary. Our previous research on longer-length multi-path models has proved that data-driven trajectory clustering to be an attractiv...

Chord labels for recorded audio are in high demand both as an end product used by musicologists and hobby musicians and as an input feature for music similarity applications. Many past algorithms for chord labelling are based on chromagrams, but distribution of energy in chroma frames is not well understood. Furthermore, non-chord notes complicate chord estimation. We present a new approach whi...

We propose a method for synthesising head motion from speech using a combination of an Input-Output Markov model (IOMM) and Gaussian mixture models trained in a supervised manner. A key difference of this approach compared to others is to model the head motion in each angle as a series of templates of motion rather than trying to recover a frame-wise function. The templates were chosen to refle...

This paper presents a new factor analyzed (FA) similarity measure between two Gaussian mixture models (GMMs). An adaptive hidden Markov model (HMM) topology is built to compensate the pronunciation variations in speech recognition. Our idea aims to evaluate whether the variation of a HMM state from new speech data is significant or not and judge if a new state should be generated in the models....