Application Of Hidden Markov Models Research Articles

On the application of hidden Markov models for enhancing noisy speech

A maximum-a-posteriori approach for enhancing speech signals which have been degraded by statistically independent additive noise is proposed. The approach is based on statistical modeling of the clean speech signal and the noise process using long training sequences from the two processes. Hidden Markov models (HMMs) with mixtures of Gaussian autoregressive (AR) output probability distributions (PDs) are used to model the clean speech signal. The model for the noise process depends on its nature. The parameter set of the HMM model is estimated using the Baum or the EM (estimation-maximization) algorithm. The noisy speech is enhanced by reestimating the clean speech waveform using the EM algorithm. Efficient approximations of the training and enhancement procedures are examined. This results in the segmental k-means approach for hidden Markov modeling, in which the state sequence and the parameter set of the model are alternately estimated. Similarly, the enhancement is done by alternate estimation of the state and observation sequences. An approximate improvement of 4.0-6.0 dB in signal-to-noise ratio (SNR) is achieved at 10-dB input SNR.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Application of hidden Markov models to automatic speech endpoint detection

Accurate detection of the boundaries of a speech utterance during a recording interval has been shown to be crucial for reliable and robust automatic speech recognition. The endpoint detection problem is fairly straightforward for high-level speech signals spoken in low-level stationary noise environments. However, these ideal conditions do not always exist. One example, where reliable word detection is difficult, is speech spoken in a mobile environment. Currently, most endpoint detection algorithms use only signal energy and duration information to perform the endpoint detection task. In this paper, an endpoint detection algorithm is presented that is based on hidden Markov model (HMM) technology. Based on a speaker-dependent speech database from four talkers, and recorded in a mobile environment under five different driving conditions (including while traveling at 60 mph with the fan on), several endpoint detection schemes were tested. The results showed that the HMM-based approach to endpoint detection performed significantly better then the energy-based system. The overall accuracy of the system using the HMM endpoint detector, when tested on the 11 word digits vocabulary (zero through nine and oh) with speech recorded in various mobile environments, was 99.4%. A top recognition performance of 99.7%, on the same conditions, was achieved by using an HMM recognizer with explicit endpointing.

Application of hidden Markov models to automatic speech endpoint detection

Accurate detection of the boundaries of a speech utterance during a recording interval has been shown to be crucial for reliable and robust automatic speech recognition. The endpoint detection problem is fairly straightforward for high-level speech signals spoken in low-level stationary noise environments (e.g. signal-to-noise ratios greater than 30 dB). However, these ideal conditions do not always exist. One example, where reliable word detection is difficult, is speech spoken in a mobile environment. Because of road, tire, fan noises, etc. detection of speech often becomes problematic. Currently, most endpoint detection algorithms use only signal energy and duration information to perform the endpoint detection task. These algorithms perform quite well with reasonable signal-to-noise ratios. However, under the harshest of conditions (e.g. in a car travelling at 60 mph with the fan on high) these algorithms begin to fail. In this paper, an endpoint detection algorithm is presented which is based on hidden Markov model (HMM) technology. The algorithm explicitly determines a set of speech endpoints based on the output of a Viterbi decoding algorithm. This algorithm was tested using a template-based speech recognition system and also using an HMM based system. Based on a speaker dependent speech database from four talkers, recorded in a mobile environment under five different driving conditions (including traveling at 60 mph with the fan on), we tested several endpoint detection schemes. The results showed that, under some conditions, the HMM-based approach to endpoint detection performed significantly better than the energy-based system. The overall accuracy of the system using the HMM endpoint detector, when trained with clean inputs and when tested on the 11 word digits vocabulary ( zero through nine and oh) with speech recorded in various mobile environments, was 99.7%. The equivalent accuracy of the energy based endpoint detector was 95.2% in a template based recognizer.

Text‐independent speaker verification using linear predictive hidden Markov models

The application of hidden Markov models to text‐independent speaker verification was studied. Linear predictive hidden Markov models have been proved to be an efficient way for statistical modeling of speech signals in all the forms of speech recognition. As was already suggested by Alan Poritz, such models can be used for statistical characterization of the talker himself. In our case, ergodic Markov models with four‐seven states, are shown to discriminate between speakers, where the spectral density of the states is characterized by eight order LPC coefficients. A large data base of 100 talkers with about 20 000 isolated digits was used. The states, as well as the optimal model dimensionality, tend to supply enough information to verify the talker with less than 3% error rate, on sufficiently large testing data. The results show, however, that most of the speaker‐dependent information is contained in the spectral densities and very little is left in the transition matrix. This may justify the vector quantization approach to the problem.

On the Hidden Markov Model and Dynamic Time Warping for Speech Recognition-A Unified View

This paper gives a unified theoretical view of the Dynamic Time Warping (DTW) and the Hidden Markov Model (HMM) techniques for speech recognition problems. The application of hidden Markov models in speech recognition is discussed. We show that the conventional dynamic time-warping algorithm with Linear Predictive (LP) signal modeling and distortion measurements can be formulated in a strictly statistical framework. It is further shown that the DTW/LP method is implicitly associated with a specific class of Markov models and is equivalent to the probability maximization procedures for Gaussian autoregressive multivariate probabilistic functions of the underlying Markov model. This unified view offers insights into the effectiveness of the probabilistic models in speech recognition applications.