Probabilistic source filter model of speech

Abstract

The human respiratory system plays a crucial role in breathing and swallow ing. However, it also plays an essential role in speech production, which is unique to humans. Speech production involves expelling air from the lungs. As the air flows from the lungs to the lips, some kinetic energy gets con verted to sound. Different structures modulate the generated sound, which is finally radiated out of the lips. The speech consists of various informa tion such as linguistic content, speaker identity, emotional state, accent, etc. Apart from speech, there are various scenarios where the sound is generated in the human respiratory system. These could be due to abnor malities in the muscles, motor control unit, or the lungs, which can directly affect generated speech as well. A variety of sounds are also generated by these structures while breathing including snoring, Stridor, Dysphagia, and Cough. The source filter (SF) model of speech is one of the earlier models of speech production. It assumes that speech is a result of filtering an excita tion or source signal by a linear filter. The source and filter are assumed to be independent. Even though the SF model represents the speech pro duction mechanism, there needs to be a tractable way of estimating the excitation and the filter. The estimation of both of them given speech falls under the general category of signal deconvolution problem, and, hence, there is no unique solution. There are several variations of the source-filter model in the literature by assuming different structures on the source/filter. There are various ways to estimate the parameters of the source and the filter. The estimated parameters are used in various speech applications such as automatic speech recognition, text to speech, speech enhancement etc. Even though the SF model is a model of speech production, it is used in applications including Parkinson s Disease classification, asthma classification...