Probabilistic-trajectory segmental HMMs

Holmes, Wendy; Russell, Martin J.

doi:10.1006/csla.1998.0048

Cited by 65 publications

(36 citation statements)

References 36 publications

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“…Stochastic segment models that deal with the duration and spectral trajectory simultaneously were proposed for phoneme-based continuous speech recognition (Ostendorf and Roukos, 1989). Probabilistic-trajectory segmental HMMs was proposed to overcome the limitations of the conventional HMMs (Holmes and Russell, 1999). In our previous studies (Lee and Lee, 2006;Lee, 2011Lee, , 2015a, we proposed high-order hidden Markov models (HO-HMMs) to improve speech recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

High-order hidden Markov model for piecewise linear processes and applications to speech recognition

Lee

Jean

2016

The Journal of the Acoustical Society of America

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The hidden Markov models have been widely applied to systems with sequential data. However, the conditional independence of the state outputs will limit the output of a hidden Markov model to be a piecewise constant random sequence, which is not a good approximation for many real processes. In this paper, a high-order hidden Markov model for piecewise linear processes is proposed to better approximate the behavior of a real process. A parameter estimation method based on the expectation-maximization algorithm was derived for the proposed model. Experiments on speech recognition of noisy Mandarin digits were conducted to examine the effectiveness of the proposed method. Experimental results show that the proposed method can reduce the recognition error rate compared to a baseline hidden Markov model.

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Section: Introductionmentioning

confidence: 99%

High-order hidden Markov model for piecewise linear processes and applications to speech recognition

Lee

Jean

2016

The Journal of the Acoustical Society of America

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“…Alternatives were proposed to capture the temporal dependence such as the segmental models [1][2][3][4][5][6]. Although these models make fewer assumptions about the correlation between adjacent frames and can improve the recognition performance, dynamic features are again used to augment the static features to obtain better performance.…”

Section: Introductionmentioning

confidence: 99%

Consistent Modeling of the Static and Time-Derivative Cepstrums for Speech Recognition Using HSPTM

Lai

Siu

2006

Chinese Spoken Language Processing

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Abstract. Most speech models represent the static and derivative cepstral features with separate models that can be inconsistent with each other. In our previous work, we proposed the hidden spectral peak trajectory model (HSPTM) in which the static cepstral trajectories are derived from a set of hidden trajectories of the spectral peaks (captured as spectral poles) in the time-frequency domain. In this work, the HSPTM is generalized such that both the static and derivative features are derived from a single set of hidden pole trajectories using the well-known relationship between the spectral poles and cepstral coefficients. As the pole trajectories represent the resonance frequencies across time, they can be interpreted as formant tracks in voiced speech which have been shown to contain important cues for phonemic identification. To preserve the common recognition framework, the likelihood functions are still defined in the cepstral domain with the acoustic models defined by the static and derivative cepstral trajectories. However, these trajectories are no longer separately estimated but jointly derived, and thus are ensured to be consistent with each other. Vowel classification experiments were performed on the TIMIT corpus, using low complexity models (2-mixture). They showed 3% (absolute) classification error reduction compared to the standard HMM of the same complexity.

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“…The segmental model [92,157,63,87,43,74,136,138,78,203,64,1] extend them to be dependent on the states as well as the state durations.…”

Section: Segmental Modelmentioning

confidence: 99%

Hidden Semi-Markov Models

Introduction to Hidden Semi-Markov Models

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As an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model (HSMM) allows the underlying stochastic process to be a semi-Markov chain. Each state has variable duration and a number of observations being produced while in the state. This makes it suitable for use in a wider range of applications. Its forwardbackward algorithms can be used to estimate/update the model parameters, determine the predicted, filtered and smoothed probabilities, evaluate goodness of an observation sequence fitting to the model, and find the best state sequence of the underlying stochastic process. Since the HSMM was initially introduced in 1980 for machine recognition of speech, it has been applied in thirty scientific and engineering areas, such as speech recognition/synthesis, human activity recognition/prediction, handwriting recognition, functional MRI brain mapping, and network anomaly detection. There are about three hundred papers published in the literature. An overview of HSMMs is presented in this paper, including modelling, inference, estimation, implementation and applications. It first provides a unified description of various HSMMs and discusses the general issues behind them. The boundary conditions of HSMM are extended. Then the conventional models, including the explicit duration, variable transition, and residential time of HSMM, are discussed. Various duration distributions and observation models are presented. Finally, the paper draws an outline of the applications.

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Probabilistic-trajectory segmental HMMs

Cited by 65 publications

References 36 publications

High-order hidden Markov model for piecewise linear processes and applications to speech recognition

High-order hidden Markov model for piecewise linear processes and applications to speech recognition

Consistent Modeling of the Static and Time-Derivative Cepstrums for Speech Recognition Using HSPTM

Hidden Semi-Markov Models

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