Connected digit speech recognition in the telephone network is becoming increasingly more important as the demand for speech technology becomes widespread. In the past few years, several highly successful techniques for recognizing spoken connected digit strings have been proposed. Although these techniques have been applied to non-telephone based speech [e.g. Texas Instruments database], they have produced high recognition performance. Further, similar levels of performances have been demonstrated using discrete density and continuous density based hidden Markov models (HMMs). The success of the vector quantized (VQ) modeling approach, in particular, is encouraging and rather important from the viewpoint of computational efficiency. This paper presents a study of connected digit recognition on telephone network based data using VQ HMMs. We investigate several factors affecting the error rate of VQ HMMs-such as maximum mutual information (MMI) training, sender modeling, and codebook size-and measure their contributions to recognition accuracy. The model architecture, number of states and transitions, is also optimized and its contribution to overall performance discussed.>

Hidden markov models (HMM's) are one of the most powerful speech recognition tools available today. Even so, the inadequacies of HMM's as a "correct" modeling framework for speech are well known. In this context, it is argued in this paper that the maximum mutual information estimation (MMIE) formulation for training is more appropriate than maximum likelihood estimation (MLE) for reducing the error rate. Corrective MMIE training is introduced. It is a very efficient new training algorithm which uses a modified version of a discrete reestimation formula recently proposed by Gopalakrishnan et al.( see IEEE Trans. Inform. Theory, Jan. 1991). Reestimation formulas are proposed for the case of diagonal Gaussian densities and their convergence properties are experimentally demonstrated. A description of how these formulas are integrated into our training algorithm is given. Using the MMIE framework for training, it is shown how weighting the contribution of different parameter sets in the computation of output probabilities introduces substantial recognition improvements. Using the TIDIGITS connected digit corpus, a large number of experiments are performed with the ideas, techniques, and algorithms presented in this paper. These experiments show that MMIE systematically provides substantial error rate reductions with respect to MLE alone and that, thanks to the new training techniques, these results can be obtained at an acceptable computational cost. The best results obtained in the experiments were 0.29% word error rate and 0.89% string error rate on the adult portion of the corpus.>

The authors describe the latest developments by the speech research group at CRIM in speaker-independent connected digit recognition, using hidden Markov models (HMMs) trained with maximum mutual information estimation (MMIE). The work presented is a continuation of work previously described by the authors (see Proc. 1991 IEEE Inf. Conf. on Acoust. Speech and Sign. Process., pp.533-536). The main differences are: (1) use of the 20-kHz TI/NIST corpus available on CD-ROM (instead of the 10-kHz distribution tape), (2) use of word models (instead of sub-word units), (3) addition of second derivative parameters, and (4) a more elaborate training procedure for codebook exponents. The experiments described were all performed on the complete adult portion of the corpus. The baseline system, using discrete HMMs and MMIE, has a 0.67% word error rate and a 2.03% string error rate. The authors describe techniques that allowed them to improve greatly the recognition rate.>

The authors describe the latest development by the speech research group at CRIM (Centre de Recherche Informatique de Montreal) in speaker-independent connected digit recognition, using hidden Markov Models (HMMs) trained with maximum mutual information estimation, in conjunction with connectionist models. The experiments described were all done on the complete adult portion of the 10 kHz speaker-independent TI/NIST connected digit database. The baseline system, using discrete HMMs and maximum likelihood estimation, has a 98.6% word recognition rate and a 96.1% string recognition rate. The authors describe techniques that made it possible to improve greatly the baseline system recognition rate. The 99.3% recognition rate and 98.0% string recognition rate were obtained with a single model per unit using discrete HMMs and recurrent neural networks. Using semi-continuous HMMs with two models per digit (one for male and one for female speakers), a 99.5% word recognition rate and a 98.4% string recognition rate were achieved.>

A hybrid coder is introduced for obtaining descriptions of speech patterns. This coder uses vector quantization (VQ) techniques on mel-scale cepstral coefficients and their derivatives together with a recurrent network (RN) for describing suprasegmental features of speech. The purpose of these features is to focus the search when hidden Markov models (HMMs) are used for speech unit or word models. Preliminary experiments of speaker-independent connected digit recognition show that using a hybrid coder based on a RN improves recognition performance.>

Combining a structural or knowledge-based approach for describing speech units with neural networks capable of automatically learning relations between acoustic properties and speech units is investigated. The authors show how speech coding can be performed by sets of multilayer neural networks whose execution is decided by a data-driven strategy. Coding is based on phonetic properties characterizing a large population of speakers. Results on speaker-independent recognition of vowels using an ear model for preprocessing are reported.>

A network for performing multiplications of two two's complement numbers is proposed. The network can be implemented in a synchronous or an asynchronous way. If the factors to be multiplied have N bits, the area complexity of the network is O(N2) for practical values of N as in the case of cellular multipliers. Due to the design approach based on a recursive algorithm, a time complexity O(log N) is achieved.

A new recursive algorithm for deriving the layout of parallel multipliers is presented. Based on this algorithm, a network for performing multiplications of two's complement numbers is proposed. The network can be implemented in a synchronous or an asynchronous way. If the factors to be multiplied have N bits, the area complexity of the network is O ( N 2 ) for practical values of N as in the case of cellular multipliers. Due to the design approach based on a recursive algorithm, a time complexity O (log N ) is achieved. It is shown how the structure can he pipelined with period complexity O (1) and used for single and double precision multiplication.