Speech processing applications use predominantly two types of modelling, models related to perception and to the source. They are always separate models on a meta-level, where we describe the motivations why we use them. In practical applications, they however have often complicated interactions, such that it becomes difficult to separate them.
- Source models characterize the objective properties of a signal. A source model can for example describe the statistical distribution of speech signals and their characteristics. One such model would be a model of the fundamental frequency; voiced signals have a fundamental frequency and we can specify the range where fundamental frequencies of speech signals can lie. Other obvious characteristics of speech signals we can model include the intensity of speech and how it can change over time, as well as the spectral envelope, what shapes are possible and how they can change over time.
- Perceptual models characterize what human listeners can hear and how much they appreciate different qualities. Perceptual models thus try to predict how humans evaluate quality. To construct perceptual models we need to ask human listeners questions like "Can you hear distortions in this signal?" or "How much is this signal distorted?". Based on the responses of human listeners, we can then make an analysis algorithm, which predicts the answer based on an analysis of the input signal.
In other words, source models explain objectively "what the world is like", where as perceptual models are evaluation models estimate subjective preference, "how good the world is".
It is not however always so clear which model is active in which situation. For example, consider a speech recognizer, to which we feed speech with a loud background noise. We must decide whether the speech recognizer would evaluate speech as a human would evaluate, or whether we want to recognize speech as best we can. It is potentially possible that the speech recognizer could tolerate noise better than humans, such that it recognizes speech also when a human would fail. The model is then objectively evaluating speech content. However, if we want for example that a humanoid robot behaves like a human, then it should not understand speech in situations where a human would not.
As another example, consider a noise attenuation task, where our objective is to restore the original speech from a corrupted sample. We can then remove noise and only evaluate objectively how close we are to the original. This is an unambiguous objetive task, where perception plays no role. More similar to the original is better. However, an alternative approach is to consider all possible sounds y, and given a noisy observation v, compute the likelihood of all possible inputs. Suppose then that our output is x, we can then compute the perceptual distortion between all possible inputs d(x,y), and assign weighting to them according to their likelihoods d(x,y)P(y|v). Finally, we can minimize the expected distortion min E[d(x,y) | P(y|v)]. We thus take into account all possible true inputs, calculate the perceptual distortion between the true inputs and the output, and weight them according to how likely they are. Now, for the same task as before, we have a perceptual criterion with which we can choose the best output. Clearly the latter is more complicated, but it is also better motivated, so we have to choose which one we want to use. The first one is based on source modelling only (model of speech and noise), while the latter is a combination of source (likelihood of different speech signals, given a noisy observation) and perceptual modelling (perceptual distortion measure).