Section: Scientific Foundations
For several decades, the probabilistic approaches have been used successfully for various tasks in pattern recognition, and more particularly in speech recognition, whether it is for the recognition of isolated words, for the retranscription of continuous speech, for speaker recognition tasks or for language identification. Probabilistic models indeed make it possible to effectively account for various factors of variability occuring in the signal, while easily lending themselves to the definition of metrics between an observation and the model of a sound class (phoneme, word, speaker, etc...).
Probabilistic formalism and modeling
The probabilistic approach for the representation of an (audio) class relies on the assumption that this class can be described by a probability density function (PDF) which associates a probability to any observation .
In the field of speech processing, the class can represent a phoneme, a sequence of phonemes, a word from a vocabulary, or a particular speaker, a type of speaker, a language, .... Class can also correspond to other types of sound objects, for example a family of sounds (word, music, applause), a sound event (a particular noise, a jingle), a sound segment with stationary statistics (on both sides of a rupture), etc.
In the case of audio signals, the observations are of an acoustical nature, for example vectors resulting from the analysis of the short-term spectrum of the signal (filter-bank coefficients, cepstrum coefficients, time-frequency principal components, etc.) or any other representation accounting for the information that is required for an efficient separation of the various audio classes considered.
In practice, the PDF is not accessible to measurement. It is therefore necessary to resort to an approximation of this function, which is usually refered to as the likelihood function. This function can be expressed in the form of a parametric model.
The models most used in the field of speech and audio processing are the Gaussian Model (GM), the Gaussian Mixture Model (GMM) and the Hidden Markov Model (HMM). But recently, more general models have been considered and formalised as graphical models.
Choosing a particular family of models is based on a set of considerations ranging from the general structure of the data, some knowledge on the audio class making it possible to size the model, the speed of calculation of the likelihood function, the number of degrees of freedom of the model compared to the volume of training data available, etc.
The determination of the model parameters for a given class is generally based on a step of statistical estimation consisting in determining the optimal value for model parameters.
The Maximum Likelihood (ML) criterion is generally satisfactory when the number of parameters to be estimated is small w.r.t. the number of training observations. However, in many applicative contexts, other estimation criteria are necessary to guarantee more robustness of the learning process with small quantities of training data. Let us mention in particular the Maximum a Posteriori (MAP) criterion which relies on a prior probability of the model parameters expressing possible knowledge on the estimated parameter distribution for the class considered. Discriminative training is another alternative to these two criteria, definitely more complex to implement than the ML and MAP criteria.
In addition to the fact that the ML criterion is only one particular case of the MAP criterion, the MAP criterion happens to be experimentally better adapted to small volumes of training data and offers better generalization capabilities of the estimated models (this is measured for example by the improvement of the classification performance and recognition on new data). Moreover, the same scheme can be used in the framework of incremental adaptation, i.e. for the refinement of the parameters of a model using new data observed for instance, in the course of use of the recognition system.
Likelihood computation and state sequence decoding
During the recognition phase, it is necessary to evaluate the likelihood function of the observations for one or several models. When the complexity of the model is high, it is generally necessary to implement fast calculation algorithms to approximate the likelihood function.
In the case of HMM models, the evaluation of the likelihood requires a decoding step to find the most probable sequence of hidden states. This is done by implementing the Viterbi algorithm, a traditional tool in the field of speech recognition. However, when the acoustic models are combined with a syntagmatic model, it is necessary to call for sub-optimal strategies, such as beam search.
When the task to solve is the classification of an observation into one class among several closed-set possibilities, the decision usually relies on the maximum a posteriori rule.
In other contexts (for instance, in speaker verification, word-spotting or sound class detection), the problem of classification can be formulated as a binary hypotheses testing problem, consisting in deciding whether the tested observation is more likely to be pertaining to the class under test or not pertaining to it. In this case, the decision consists in acceptance or rejection, and the problem can be theoretically solved within the framework of Bayesian decision by calculating the ratio of the PDFs for the class and the non-class distributions, and comparing this ratio to a decision threshold.
In theory, the optimal threshold does not depend on the class distribution, but in practice the quantities provided by the probabilistic models are not the true PDFs, but only likelihood functions which approximate the true PDFs more or less accurately, depending on the quality of the model of the class.
The optimal threshold must be adjusted for each class by modeling the behaviour of the test on external (development) data.
In the past years, increasing interest has focused on graphical models for multi-source audio signals, such as polyphonic music signals. These models are particularly interesting, since they enable a formulation of music modelisation in a probabilistic framework.
It makes it possible to account for more or less elaborate relationship and dependencies between variables representing multiple levels of description of a music piece, together with the exploitation of various priors on the model parameters.
Following a well-established metaphore, one can say that the graphical model expresses the notion of modularity of a complex system, while probability theory provides the glue whereby the parts are combined. Such a data structure lends itself naturally to the design of efficient general-purpose algorithms.
The graphical model framework provides a way to view a number of existing models (including HMMs) as instances of a common formalism and all of them can be addressed via common machine learning tools.
A first issue when using graphical models is the one of the model design, i.e. the chosen variables for parameterizing the signal, their priors and their conditional dependency structure.
The second problem, called the inference problem, consists in estimating the activity states of the model for a given signal in the maximum a posteriori sense. A number of techniques are available to achieve this goal (sampling methods, variational methods belief propagation, ...), whose challenge is to achieve a good compromise between tractability and accuracy  .