To introduce you to the signal processing and statistical techniques that are used in processing speech signals and to give you an understanding of how these techniques are used in the coding, synthesis and recognition of speech. It is assumed that you will have already taken one or more courses in digital signal processing such as third year DSP or ASP. The prerequisites are to be comfortable with thinking and working with analysis in time, frequency and z-transform domains, to have a good knowledge of digital filters, and the main elements of linear algebra.

By the end of the module, you will be able to: -apply signal processing techniques appropriately for the coding, synthesis and recognition of speech signals; -implement dynamic programming in pattern recognition applications. -implement statistical modelling in pattern recognition applications. -implement inference techniques in pattern recognition applications

The human vocal and auditory systems; Characteristics of speech signals: phonemes, prosody, IPA notation; Lossless tube model of speech production; Time and frequency domain representations of speech; Window characteristics and time/frequency resolution tradeoffs; Properties of digital filters: mean log response, resonance gain and bandwidth relations, bandwidth expansion transformation, all-pass filter characteristics; Autocorrelation and covariance linear prediction of speech; Optimality criteria in time and frequency domains; Alternate LPC parametrisation; Speech coding: PCM, ADPCM, CELP; Speech synthesis: an introduction; Time domain pitch and speech modification; Speech recognition: hidden Markov models and associated recognition and training algorithms; Dynamic Programming; Language modelling; Large vocabulary recognition; Acoustic preprocessing for speech recognition.