S. Young. The htk hidden markov model toolkit. Technical Report TR152, Cambridge University Engineering Department CUED, 1993. ACCEPTED FOR PUBLICATION: JNRSAS, (IN PRESS)  Home/Search   Document Not in Database   Summary   Related Articles   Check

....of the non stationary parts. The BACD use for the first type of segmentation was studied and discussed in [11] The probability of properly determination all changes is not very high about 87 . As to the latter type of signal segmentation, the semiautomatic HTK based (Hidden Markov Model Toolkit [10]) segmentation was used. The comparison of HTK segmentation, and the segmentation based on the combination of HTK BACD can be seen from Fig. 3 and Tab. 5. In Fig. 3 the estimated # m gained by manual segmentation, verified by listening and spectrogram inspection) HTK segmentation, and ....

S.J. Young, HTK: Hidden Markov Model Toolkit, Cambridge University, UK, 1992.

....planning task: as shown, it achieves less than 60 word accuracy on fluent utterances collected in problem solving dialogues with the TRAINS 95 system. In those experiments, the acoustic model and the class based language model were trained on ATIS data. Similarly, a recognizer built using HTK [9] on human human speech (Trains Dialogue Corpus) performed poorly on computer human speech. SPEECHPP can help in precisely these scenarios. With regard to the small margins of improvement from our fertility models, we observe that the amounts of training data we have used are still largely ....

S. J. Young and P. C. Woodland. HTK: Hidden Markov Model Toolkit. Entropic Research Lab., Washington, D.C., 1993.

....Coding (LPC) Rabiner, 1989] However, many sets of parameters are possible. For example, parameter types include linear prediction filter coefficients, linear prediction reflection coefficients, LPC Cepstral coefficients, mel frequency Cepstral coefficients, and mel filter bank channel outputs [Young and Woodland, 1993]. Describing the speech signal in terms of a sequence of spectral vectors allows for further discretization using vector quantization [Gray, 1984] This process takes a vector of spectral parameters and finds the nearest neighbor in a table (code book) of N prototypical vectors, statically ....

....the states themselves. 2 Furthermore, in the above definition, we focused on a discrete output probability distribution; however, continuous densities are also possible. For continuous HMMs, densities are usually constructed as Gaussian mixtures for purposes of straightforward computation(e.g. [Young and Woodland, 1993]) A compromise between the two approaches, called semi continuous HMMs (SCHMMs) has been defined by Huang [Huang and Jack, 1989] 2 These two models are the probabilistic counterparts to Moore and Mealy machines, respectively (c.f. Hopcroft and Ullman, 1979] 19 It shouldbe noted that the ....

Stephen J. Young and Philip C. Woodland, HTK: Hidden Markov Model Toolkit, Entropic Research Laboratory, Washington, D.C., 1993. 70

....information to help classification. The purpose of this paper is to introduce a two dimensional hidden Markov model (2 D HMM) as a general framework to build context dependent classifiers. Hidden Markov models have earned their popularity mostly from successful application to speech recognition [4, 5, 6]. Despite the weakness of the Markovian assumption as applied to speech, they have proven to be a powerful method in speech processing. The probability mechanism is as follow: at any discrete unit of time, the system is assumed to exist in one of a finite set of states. Within each state there is ....

.... at block (i Gamma 1; j) state n at block (i; j Gamma 1) and state l at block (i; j) given the observed feature vectors, classes, and model OE (p) In the case of one dimensional HMM as used in speech recognition, computationally efficient formulas exist for calculating Lm (k) and Hm;l (k) [6]. For 2 D HMM, however, the computation of Lm(i; j) and H m;n;l (i; j) is not feasible, due to the two dimensional transition probabilities. The next section will discuss why this is so and how to reduce the computational complexity. 4. COMPUTATIONAL COMPLEXITY The EM procedure outlined in the ....

[Article contains additional citation context not shown here]

S. Young, J. Jansen, J. Odell, D. Ollason and P. Woodland, HTK - Hidden Markov Model Toolkit, Cambridge University, 1995.

....according to the equations in section 2. Five state left to right hidden Markov models were used to model each of the digits. Emission probabilities were modeled as multivariate Gaussian with full covariance matrices. Training and recognition were conducted using the Entropics HTK tool box [30]. Discriminant analysis is performed to reduce the feature dimension in the following steps: 1. Model parameters are estimated using the training data. 2. Using the model parameters and the segment labels, the most likely sequence of states is determined for the training data. 3. Each state is ....

S. J. Young, P. C. Woodland, and W. J. Byrne, "HTK -- hidden markov model toolkit," Cambridge University Engineering Department Speech Group and Entropic Research Laboratories Inc.

....for the highest resolution, there is an extra mixed class besides the original classes. 3 The Algorithm We estimate the parameters of the multiresolution model by the EM algorithm [1] Due to computation complexity, we apply a suboptimal estimation algorithm, the Viterbi training algorithm [5]. The algorithm iteratively improves the estimation by searching for the combination of states with the maximum a posteriori probability given the observed feature vectors and the current model estimation. The states are then regarded as the true states to update the model estimation. Since our ....

S. Young, J. Jansen, J. Odell, et al., HTK - Hidden Markov Model Toolkit, Cambridge University, 1995.

....: Pr(x t 1 ; x t 2 ; Delta Delta Delta ; x T js t = i; 2:9) that is, the probability of the trailing observation from time t onwards conditioned on the final state being i. The forward algorithm consists of the following recursion to find the probability of an observation [15] 14] [16]: Initialization: ff 1 (1) 1 ff 1 (j) a 1j f j (x 1 ) 2.10) Recursion: For t = 2; 3; Delta Delta Delta ; T , and for j = 2; 3; Delta Delta Delta ; N Gamma 1 ff t (j) N Gamma1 X i=2 ff t Gamma1 (i)a ij ]f i (x t ) 2:11) Final Probability: Pr(Xj ) ff T (N) N Gamma1 X ....

....our data set contains data subsets corresponding to different categories we wish to model. More importantly, it is not always possible to robustly segment the data into different categories to then implement the single model training algorithm described above. The embedded Baum Welch algorithm [16] consists of a technique to train several models from a unique source of data by updating all models simultaneously. The algorithm works by linking several single models together to create a composite HMM which reflects the classification of different sections of the data set (i.e. different words ....

[Article contains additional citation context not shown here]

S. Young, J. Jansen, J. Odell, D. Ollason, and P. Woodland. HTK - Hidden Markov Model Toolkit. Entropic Research Laboratory, Inc.

....Estimating the parameters of an HMM from the training data has been discussed at length in the literature. We have used the standard Baum Welch re estimation algorithm to obtain initial parameters of the model [3] 4] and then improved initial estimates with the embedded BaumWelch algorithm [5]. Once the systems have been trained, the performance is assessed by applying a Viterbi decoder to a set of testing data and producing a set of transcription labels [6] Efficient software implementation of these learning algorithms was done using the Hidden Markov Toolkit (HTK) version 2.0) ....

....by applying a Viterbi decoder to a set of testing data and producing a set of transcription labels [6] Efficient software implementation of these learning algorithms was done using the Hidden Markov Toolkit (HTK) version 2. 0) developed at Cambridge University and Entropic Research Laboratories) [5]. How to choose a model structure (i.e. number of states, output distribution types, and model topology) is not clear for the signals we have chosen to measure. In order to investigate the performance of different model types, and also allow model types to be user dependent, we have considered a ....

S. Young, J. Jansen, J. Odell, D. Ollason, and P. Woodland. HTK - Hidden Markov Model Toolkit. Entropic Research Laboratory, Inc.

....robustly interpreting spontaneous utterances in a dialogue with a human. 1. INTRODUCTION Existing methods for continuous speech recognition do not perform as well on spontaneous speech as we would hope. Even state of the art recognizers such as Sphinx II [7] 1 and a recognizer built using HTK [14] 2 achieve less than 60 word accuracy on fluent speech collected from conversations about a specific problem with the Trains 95 system [1] Here are a few examples of the kinds of errors that occur when recognizing spontaneous utterances. They are drawn from problem solving dialogues that we ....

S. J. Young and P. C. Woodland. HTK: Hidden Markov Model Toolkit. Entropic Research Laboratory, Washington, D.C., 1993.

....planning task: as shown, it achieves less than 60 word accuracy on fluent utterances collected in problem solving dialogues with the TRAINS 95 system. In those experiments, the acoustic model and the class based language model were trained on ATIS data. Similarly, a recognizer built using HTK [9] on human human speech (Trains Dialogue Corpus) performed poorly on computer human speech. SPEECHPP can help in precisely these scenarios. With regard to the small margins of improvement from our fertility models, we observe that the amounts of training data we have used are still largely ....

S. J. Young and P. C. Woodland. HTK: Hidden Markov Model Toolkit. Entropic Research Lab., Washington, D.C., 1993.

....L(x) M X i=1 T (i) 1 (i) i b i (x 1 ) 1 i M t 1 (j) b j (x t 1 ) M X i=1 t (i)a ij 1 t T Gamma 1; 1 j M t (i) is the likelihood of the observations x 1 : x t given that at time t the state is i. For details, see any of the references on speech recognition [10, 11, 12, 16]. Of particular interest to this paper is the most likely sequence of states fs t g T t=1 given the observation sequence fx t g T t=1 . This is typically computed using dynamic programming (the Viterbi algorithm [17] Estimation of 1 D HMM model parameters is usually performed according to ....

....of the state parameters from each training observation sequence, weighted by the likelihood of each possible state sequence that could have caused it. This results in maximum likelihood parameter estimates. An approximation to maximum likelihood training is what is often termed Viterbi training [16], in which each observation is assumed (with weight of 1) to have resulted from the single most likely state sequence that might have caused it. While more efficient computationally, Viterbi training does not in general result in maximum likelihood estimates. Note that an intermediate technique ....

[Article contains additional citation context not shown here]

S. Young, J. Jansen, J. Odell, D. Ollason and P. Woodland, HTK - Hidden Markov Model Toolkit, Cambridge University, 1995.

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S. Young. The htk hidden markov model toolkit. Technical Report TR152, Cambridge University Engineering Department CUED, 1993. ACCEPTED FOR PUBLICATION: JNRSAS, (IN PRESS)

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Young, S.J. Woodland P.C., and Byme W.J. HTK: Hidden Markov Model Toolkit. V1.5. Entropic Research Laboratories Inc., 1993.

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