The REMOS (REverberation MOdeling for Speech recognition) concept for reverberation-robust distanttalking speech recognition [1] is presented in this paper. REMOS extends a conventional hidden Markov model (HMM) trained on close-talking data with a reverberation model describing the acoustical environment. The combination of both models is performed during recognition to match the reverberant o...

Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatics method for the prediction of peptide binding to T-cell molecules. The major T-cell contributors are selected for the dataset preparation due to its availability and ...

In speaker adaptation for HMM-based speech synthesis, model adaptation and adaptive training techniques play key roles. For reducing dependency on an initial model and adapting the model to wide-ranging target speakers, we propose speaker adaptation and adaptive training algorithms based on ESAT algorithm for HMM-based speech synthesis. The ESAT algorithm estimates contributing rate of several ...

Text-to-speech (TTS) is currently a mature technology that is used in many applications. Some modules of a TTS depend on the language and, while there are many public resources for English, the resources for some underrepresented languages are still limited. This work describes the development of a complete TTS system for Brazilian Portuguese which expands the already available resources. The s...

Classification and statistical learning by hidden markov model has achieved remarkable progress in the past decade. They have been applied in many areas like speech recognition and handwriting recognition. However, learning by Hidden Markov Model (HMM) is still restricted to supervised problems. In this paper, we propose a new learning method 2484 Badreddine Benyacoub et al. based on HMM techni...

This paper proposes a new class of hidden Markov model (HMM) called multiple-regression HMM (MRHMM) that utilizes auxiliary features such as fundamental frequency ( ) and speaking styles that affect spectral parameters to better model the acoustic features of phonemes. Though such auxiliary features are considered to be the factors that degrade the performance of speech recognizers, the propose...

1 Abstract 1.1 Motivation Complete forward-backward (Baum-Welch) hidden Markov model training cannot take advantage of the linear space, divide-and-conquer sequence alignment algorithms because of the examination of all possible paths rather than the single best path. This paper discusses the implementation and performance of checkpoint-based reduced space sequence alignment in the SAM Hidden M...

Segmentation and labeling of high dimensional time series data has wide applications in behavior understanding and medical diagnosis. Due to the difficulty of obtaining a large amount the label information, realizing this objective in an unsupervised way is highly desirable. Hidden Semi-Markov Model (HSMM) is a classical tool for this problem. However, existing HSMM and its variants typically m...