Clustering algorithm for F0 curves based on hidden Markov models

نویسندگان

  • Damien Lolive
  • Nelly Barbot
  • Olivier Boëffard
چکیده

This article describes a new unsupervised methodology to learn F0 classes using HMM on a syllable basis. A F0 class is represented by a HMM with three emitting states. The unsupervised clustering algorithm relies on an iterative gaussian splitting and EM retraining process. First, a single class is learnt on a training corpus (8000 syllables) and it is then divided by perturbing gaussian means of successive levels. At each step, the mean RMS error is evaluated on a validation corpus (3000 syllables). The algorithm stops automatically when the error becomes stable or increases. The syllabic structure of a sentence is the reference level we have taken for F0 modelling even if the methodology can be applied to other structures. Clustering quality is evaluated in terms of cross-validation using a mean of RMS errors between F0 contours on a test corpus and the estimated HMM trajectories. The results show a pretty good quality of the classes (mean RMS error around 4Hz).

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Abnormality Detection in a Landing Operation Using Hidden Markov Model

The air transport industry is seeking to manage risks in air travels. Its main objective is to detect abnormal behaviors in various flight conditions. The current methods have some limitations and are based on studying the risks and measuring the effective parameters. These parameters do not remove the dependency of a flight process on the time and human decisions. In this paper, we used an HMM...

متن کامل

Unsupervised HMM classification of F0 curves

This article describes a new unsupervised methodology to learn F0 classes using HMM models on a syllable basis. A F0 class is represented by a HMM with three emitting states. The clustering algorithm relies on an iterative gaussian splitting and EM retraining process. First, a single class is learnt on a training corpus (8000 syllables) and it is then divided by perturbing gaussian means of suc...

متن کامل

Motion Recognition and Spinal Monitoring Based on Hidden Markov Models and K-means Clustering Using Wearable Sensors

As the internet of things (IoT) turns into the focus of software market, motion recognition has become popular in many areas, such as health care and real-time monitoring. Thus, this paper presents a motion recognition and spinal monitoring method using wearable sensors based on Hidden Markov Models (HMM) and K-means. To predict six different motions, i.e., walking, walking-upstairs, walking-do...

متن کامل

Speech enhancement based on hidden Markov model using sparse code shrinkage

This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...

متن کامل

An Adaptive Approach to Increase Accuracy of Forward Algorithm for Solving Evaluation Problems on Unstable Statistical Data Set

Nowadays, Hidden Markov models are extensively utilized for modeling stochastic processes. These models help researchers establish and implement the desired theoretical foundations using Markov algorithms such as Forward one. however, Using Stability hypothesis and the mean statistic for determining the values of Markov functions on unstable statistical data set has led to a significant reducti...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007