Robust smoothing methods for discrete hidden Markov models

Schwartz, Richard; Kimball, Owen; Kubala, Francis; Feng, Ming-Whei; Chow, Yen‐Lu; Barry, Chris; Makhoul, John

doi:10.1109/icassp.1989.266485

Cited by 31 publications

(12 citation statements)

References 8 publications

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“…Because of well-known techniques to smooth ML estimates we are able to train models with just one training token [31]. We are therefore able to train phone, lefvright context and triphone models.…”

Section: Elliptical Basis Functions Networkmentioning

confidence: 99%

A hybrid segmental neural net/hidden Markov model system for continuous speech recognition

Zavaliagkos

Zhao²,

Schwartz³

et al. 1994

IEEE Trans. Speech Audio Process.

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Absfrucf-The current state-of-the-art in large-vocabulary, continuous speech recognition is based on the use of hidden Markov models (HMM). In an attempt to improve over HMM performance, we developed a hybrid system that combines the advantages of neural networks and HMM using a multiple hypothesis (or N-best) paradigm. The connectionist component of the system, the segmental neural net (SNN), models all the frames of a phonetic segment simultaneously, thus overcoming the well-known conditional-independence limitation of HMM's. In the paper, we describe the hybrid system and we discuss various aspects of SNN modeling, including network architectures, training algorithms and context modeling. Finally, we evaluate the hybrid system by performing several speaker-independent experiments with the DARPA Resource Management (RM) corpus, and we demonstrate that the hybrid system shows a consistent improvement in performance over the baseline HMM system. I. I NTRODUCTI ONONTINUOUS speech recognition (CSR) is in principle C a massive search procedure among all possible sentences (word sequences) allowed by the vocabulary and the grammar, and all possible alignments of each sentence with the input speech, to find the sentence and the alignment that are the most likely given the input speech. Because of the variability of individual words in time and the fact that words are connected, a successful CSR system must deal with many interrelated aspects of time modeling: the variability of parameters within a speech unit (such as a word or a phoneme) as a function of time, the variability that is introduced by the acoustic dependence of one phoneme on neighboring phonemes, and the need to model the input speech as a sequence of concatenated speech units, allowing for different possible alignments or segmentations. Finally, for each possible alignment between a hypothesized sequence of speech units and the input speech, the system must produce a global match score to indicate how closely it matches the input speech. The global score consists of a combination of the scores of the individual speech units.State-of-the-art CSR systems are based on the use of hidden Markov models (HMM) to model phonemes in context. Fig. 1 gives a summary of the basic functioning of a HMM and how it deals with the time modeling problems stated above.' Basic to are with BBN Systems and IEEE Log Number 9214815.'An extensive description of HMM is out of the scope of this paper.The interest reader may refer to [l] for a tutorial, and to [2] for a more detailed presentation. An easily readable overview of an HMM-based speech recognition system can be found at [3].Technologies, Cambridge, MA 02138. spectral frat" Fig. 1. Basic functioning of a hidden Markov model (HMM). Model: Thephonetic models use a number of states (three in this example) and left to right transitions. Associated with each transition is the probability of moving from one state to another. Associated with each state is a probability density function (pdf) the spectral features p(xlstate), f...

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Section: Elliptical Basis Functions Networkmentioning

confidence: 99%

A hybrid segmental neural net/hidden Markov model system for continuous speech recognition

Zavaliagkos

Zhao²,

Schwartz³

et al. 1994

IEEE Trans. Speech Audio Process.

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“…Although many interesting approaches to the smoothing problem were explored (e.g. Schwartz et al, 1989), only the multiple codebook approach of Gupta, Lennig and Mermelstein (1987) had a major impact.…”

Section: Introductionmentioning

confidence: 99%

Tree-structured vector quantization for speech recognition

Barszcz

Chen

Kenny

2000

Computer Speech & Language

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“…Because only a small number of the possible feature vector values will occur in any training set, it is important to use probability estimation and smoothing techniques that not only will model the training data well but also will model other possible occurrences in future unseen data. A number of probability estimation and smoothing techniques have been developed that strike a good compromise between computation, robustness, and recognition accuracy and have resulted in error rate reductions of about 20 percent compared to the discrete HMMs presented in this paper (10,(21)(22)(23).…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

State of the art in continuous speech recognition.

Makhoul¹,

Schwartz²

1995

Proc. Natl. Acad. Sci. U.S.A.

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In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speakerindependent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds.More and more, speech recognition technology is making its way from the laboratory to real-world applications. Recently, a qualitative change in the state of the art has emerged that promises to bring speech recognition capabilities within the reach of anyone with access to a workstation. High-accuracy, real-time, speaker-independent, continuous speech recognition for medium-sized vocabularies (a few thousand words) is now possible in software on off-the-shelf workstations. Users will be able to tailor recognition capabilities to their own applications. Such software-based, real-time solutions usher in a whole new era in the development and utility of speech recognition technology.As is often the case in technology, a paradigm shift occurs when several developments converge to make a new capability possible. In the case of continuous speech recognition, the following advances have converged to make the new technology possible: * higher-accuracy continuous speech recognition, based on better speech modeling techniques; * better recognition search strategies that reduce the time needed for high-accuracy recognition; and * increased power of audio-capable, off-the-shelf workstations. The paradigm shift is taking place in the way we view and use speech recognition. Rather than being mostly a laboratory endeavor, speech recognition is fast becoming a technology that is pervasive and will have a profound influence on the way humans communicate with machines and with each other. This paper focuses on speech modeling advances in continuous speech recognition, with an exposition of hidden Markov models (HMMs), the mathematical backbone behind these advances. While knowledge of properties of the speech signal and of speech perception have always played a role, recent improvements have relied largely on solid mathematical and probabilistic modeling methods, especially the use of HMMs for modeling speech sounds. These methods are capable of modeling time and spectral variability simultaneously, and the model parameters can be estimated automatically from given training speech data. The traditional processes of segmentation and labeling of speech sounds are now merged into a single probabilistic proce...

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Robust smoothing methods for discrete hidden Markov models

Cited by 31 publications

References 8 publications

A hybrid segmental neural net/hidden Markov model system for continuous speech recognition

A hybrid segmental neural net/hidden Markov model system for continuous speech recognition

Tree-structured vector quantization for speech recognition

State of the art in continuous speech recognition.

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