Speech Signal Analysis

Abstract

AbstractIntelligent systems possess the capability to model and solve many problems of practical importance. The best way to understand these systems is do design and develop such systems which exposes their various advantages and disadvantages. This chapter presents the basic analysis technique of speech signals that would further help us in using speech as a medium of developing intelligent systems. In this chapter we study the manner in which we may highlight and extract useful features out of a given speech signals. We discuss the analysis techniques in two heads. The first head consist of the bank of filters approach. Here we present the Fourier, Short Time Fourier and Wavelet Analysis which extract interesting features. Here we would stress the importance of time and frequency domain. In the other head we would discuss the Linear Predictive Coding (LPC) methods. We discuss the manner in which the linear coding helps in analysis. We even discuss the general speech parameters that facilitate good recognition in these intelligent systems.KeywordsPower Spectral DensitySpeech SignalShort Time Fourier TransformSpeaker VerificationSpeaker IdentificationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.