Abstract: An improved speech coder provides a more natural sounding replication of speech by modifying the mean-squared error criterion for the selected speech coder parameters. Specifically, the modification emphasizes the signal components that the speech coder has difficulty matching, i.e. the high frequencies. This emphasis is constrained to certain limitations to avoid over-emphasizing the speech.

Abstract: A Vector-Sum Excited Linear Predictive Coding (VSELP) speech coder provides improved quality and reduced complexity over a typical speech coder. VSELP uses a codebook which has a predefined structure such that the computations required for the codebook search process can be significantly reduced. This VSELP speech coder uses single or multi-segment vector quantizer of the reflection coefficients based on a Fixed-Point-Lattice-Technique (FLAT). Additionally, this speech coder uses a pre-quantizer to reduce the vector codebook search complexity and a high-resolution scalar quantizer to reduce the amount of memory needed to store the reflection coefficient vector codebooks. Resulting in a high quality speech coder with reduced computations and storage requirements.

Abstract: In a statistical based speech recognition system, one of the key issues is the selection of the Hidden Markov Model that best matches a given sequence of feature observations. The problem is usually addressed by the calculation of the maximum likelihood, ML, state sequence by means of a Viterbi or other decoder. Noise or inadequate training can produce a ML sequence associated with a Hidden Markov Model other than the correct model. The method of the present invention provides improved robustness by combining the standard ML state sequence score (416) with an additional path score (418) derived from the dynamics of the ML score as a function of time. These two scores, when combined, form a hybrid metric (420) that, when used with the decoder, optimizes selection of the correct Hidden Markov Model (422).

Abstract: A digital speech coding method uses an Rth-order filter to model the frequency response of multiple filters, thereby, providing a filter which offers the control of multiple filters without the complexity of multiple filters. The Rth-order filter can be used as a spectral noise weighting filter or a combination of a short-term predictor filter and a spectral noise weighting filter, referred to as the spectrally noise weighted synthesis filter, depending on which embodiment is employed. In general, the method models the frequency response of L Pth-order filters by a single Rth-order filter, where the order R<L.times.P. Thus, this method increases the control of a speech coder filter without a corresponding increase in the complexity of the speech coder.

Abstract: An Rth-order filter models the frequency response of multiple filters, to provide a filter which offers the control of multiple filters without the complexity of multiple filters. The Rth-order filter can be used as a spectral noise weighting filter or a combination of a short-term predictor filter and a spectral noise weighting filter, referred to as the spectrally noise weighted synthesis filter, depending on which embodiment is employed. In general, the method models the frequency response of L Pth-order filters by a single Rth-order filter, where the order R<L.times.P. Thus, this method increases the control of a speech coder filter without a corresponding increase in the complexity of the speech coder.