Most existing reciprocal recommender systems use either profile similarity or interaction similarity to recommend new matches, assuming that user preferences are static and ignoring temporal aspects of user behaviour. This paper takes a different approach, and addresses the issue of representing user preferences as dynamic. We introduce a new representation for changes in user preferences and u...

This paper presents a statistical method that estimates a sequence of musical notes from a vocal F0 trajectory. Since the onset times and F0s of sung notes are considerably deviated from the discrete tatums and pitches indicated in a musical score, a score model is crucial for improving timefrequency quantization of the F0s. We thus propose a hierarchical hidden semi-Markov model (HHSMM) that c...

This paper presents a method by which a reinforcement learning agent can solve the incomplete perception problem using memory. The agent uses a hidden Markov model (HMM) to represent its internal state space and creates memory capacity by splitting states of the HMM. The key idea is a test to determine when and how a state should be split: the agent only splits a state when doing so will help t...

In this paper, we model a stock dynamic using Hidden Markov Model (HMM) where weekly return is normally distributed with mean and variance depending on the hidden random variable representing the stock trend. Using Expectation-Maximization algorithm, we estimate all the parameters and design a simple trading strategy based on it. We then compare its performance with Buy and Hold and Resistance ...

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...