Abstract: A method of enhancing distorted signal having a speech and noise component, with a processing device having memory with stored training information T, comprising a step of removing noise in spectrum domain according to a noise and speech model to obtain a clear signal spectrum, wherein the training information T comprises dictionaries of compact spectra prototypes of speech and noise, speech gains and noise gains forming together composite states and it comprises probabilities of state history. Dictionaries of compact spectra prototypes comprise compact spectra prototypes of a first dimension P1 and of a second dimension P2 where second dimension P2 is higher or equal to the first dimension P1.

Abstract: A method of enhancing distorted signal having a speech and noise component, with a processing device having memory with stored training information T, comprising a step of removing noise in spectrum domain according to a noise and speech model to obtain a clear signal spectrum, wherein the training information T comprises dictionaries of compact spectra prototypes of speech and noise, speech gains and noise gains forming together composite states and it comprises probabilities of state history. Dictionaries of compact spectra prototypes comprise compact spectra prototypes of a first dimension P1 and of a second dimension P2 where second dimension P2 is higher or equal to the first dimension P1.

Abstract: Systems and methods for adjusting a discrete acoustic model complexity in an automatic speech recognition system. In some cases, the systems and methods include a discrete acoustic model, a pronunciation dictionary, and optionally a language model or a grammar model. In some cases, the methods include providing a speech database comprising multiple pairs, each pair including a speech recording called a waveform and an orthographic transcription of the waveform; constructing the discrete acoustic model by converting the orthographic transcription into a phonetic transcription; parameterizing the speech database by transforming the waveforms into a sequence of feature vectors and normalizing the sequences of the feature vectors; and training the acoustic model with the normalized sequences of the feature vectors, wherein the complexity PI of the discrete acoustic model is further adjusted through a procedure that uses a given generalization coefficient N. Other implementations are described.

Abstract: The present invention relates to a method and apparatus to be used in designing an index assignment matrix for use in multiple description coding of an information signal. The bandwidth of the index assignment matrix is selected in dependence of transmission condition information relating to a transmission condition of a communication channel onto which a description of the information signal can be transmitted.