An effective multi-pitch tracking algorithm for noisy speech is critical for auditory processing. However, the performance of existing algorithms is not satisfactory. We have developed a robust algorithm for multi-pitch tracking of noisy speech based on statistical anticipation. By combining an improved channel and peak selection method, a new integration method for extracting periodicity infor...

In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text an...

In this paper, we present an ongoing work which aims at synthesizing speech-laugh sentences in realtime. To do so, the Hidden Markov Model (HMM)based speech-laugh synthesis system will be used along with the MAGE software library. First results are available online on tcts.fpms.ac.be/~laughter/ laughterWorkshop15.

This paper deals with the transcription of vocal melodies in music recordings. The proposed system relies on two distinct pitch estimators which exploit characteristics of the human singing voice. A Hidden Markov Model (HMM) is used to fuse the pitch estimates and make voicing decisions. The resulting performance is evaluated on the MIREX 2006 Audio Melody Extraction data.

There are many shared attributes between existing iterative aligners and Hidden Markov Model (HMM). A learning algorithm of HMM called Viterbi is the same as the iteration of DP-matching of iterative aligners. HMM aligners can use the result of an iterative aligner initially, incorporate the similarity score of amino acids, and apply the detailed gap cost systems to improve the matching accurac...

Hierarchical Hidden Markov Model (HHMM) parsers have been proposed as psycholinguistic models due to their broad coverage within human-like working memory limits (Schuler et al., 2008) and ability to model human reading time behavior according to various complexity metrics (Wu et al., 2010). But HHMMs have been evaluated previously only with very wide beams of several thousand parallel hypothes...

This work presents a study on the capacity of hidden Markov model (HMM) to serve as a unified approach towards two challenging problems, partially occluded faces and variant poses in face recognition. In the training stage, parameters of HMM are trained based on frontal, fully observed face images. In the testing stage, the recognition rates of HMM based face recognition system are computed bas...

The objective of this paper is to automatically build a Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998) structure to detect semantic patterns from data with an unknown structure by exploring the natural hierarchical decomposition embedded in the data. The problem is important for effective motion data representation and analysis in a variety of applications: film and game making, mi...

This paper describes a methodology for the statistical modeling of music works. Starting from either the representation of the symbolic score or the audio recording of a performance, a hidden Markov model is built to represent the corresponding music work. The model can be used to identify unknown recordings and to align them with the corresponding score. Experimental evaluation using a collect...