To estimate the emission parameters in hidden Markov models one commonly uses the EM algorithm or its variation. Our primary motivation, however, is the Philips speech recognition system wherein the EM algorithm is replaced by the Viterbi training algorithm. Viterbi training is faster and computationally less involved than EM, but it is also biased and need not even be consistent. We propose an...

We consider estimation of the emission parameters in hidden Markov models. Commonly, one uses the EM algorithm for this purpose. However, our primary motivation is the Philips speech recognition system wherein the EM algorithm is replaced by the Viterbi training algorithm. Viterbi training is faster and computationally less involved than EM, but it is also biased and need not even be consistent...

Using the Viterbi decoding algorithm with repeat request for convolutional codes is proposed, and the resuking performance is analyzed by random coding aud generatiug function arguments and by simtdation. It is &own that the reliability function of tbe proposed decoding algorithm is asymptotically twice that of the Viterbi decoding algorithm without repeat request, and that in certain practical...

For a given computational complexity, the Viterbi algorithm applied on the discrete representation of the state space provided by a standard particle filtering, outperforms the particle filtering. However, the computational complexity of the Viterbi algorithm is still high. In this paper, we propose to use the M and T algorithms in order to reduce the computational complexity of the Viterbi alg...

The lazy Viterbi decoder is a maximum-likelihood decoder for block and stream convolutional codes. For many codes of practical interest, under reasonable noise conditions, the lazy decoder is much faster than the original Viterbi decoder. For a code of constraint length , the lazy algorithm is about 50% faster than an optimized implementation of the Viterbi decoder whenever SNR dB. Moreover, wh...

We present an efficient algorithm for estimating hidden state sequences in imprecise hidden Markov models (iHMMs), based on observed output sequences. The main difference with classical HMMs is that the local models of an iHMM are not represented by a single mass function, but rather by a set of mass functions. We consider as estimates for the hidden state sequence those sequences that are maxi...