Patent ID: 7457748

Claim:
Method of automatic processing of a speech signal comprising: an automatic step of determination of at least one sequence of probability models coming from a finite directory of models, each sequence describing the probability of acoustic production of a sequence of symbolic units of a phonological nature coming from a finite alphabet, the said sequence of symbolic units corresponding to at least one given text and the said probability models each including an observable random process corresponding to the acoustic production of symbolic units and a non-observable random process having known probability properties, so-called Markov properties; a step of processing a speech signal to determine a sequence of digital data strings, known as acoustic strings, representing acoustic properties of the speech signal; a step of alignment between the said sequence of acoustic strings and the said at least one sequence of models, each model being associated with a sub-sequence of acoustic strings, forming an acoustic segment, and each value of the non-observable process of each model being associated with a sub-sequence of acoustic strings forming an acoustic sub-segment in order to deliver a sequence of non-observable process values associating a value with each acoustic string, known as an aligned sequence; a step of determination of a confidence index of acoustic alignment for each association between a model of the sequence and an acoustic segment, known as a model alignment confidence index, and corresponding to an estimate of the probability a posteriori of the model given the observation of the corresponding acoustic segment, known as the a posteriori model probability, said step of determination of a confidence index of acoustic alignment providing data of the confidence index of acoustic alignment, characterised in that each step of determination of an alignment confidence index for a model comprises the calculation of the value of the said index at least from a combination of: the probability of observation of each acoustic string given the value of the non-observable process, known as the model probability and determined from known characteristic parameters of the probability model; probabilities of production a priori of all the models of the said directory, independently of one another, known as the a priori model probabilities; and the analytical estimation of the average duration of occupancy of the values of the non-observable process of the model; and a step of delivering a final sequence of labeled strings comprised of speech data.