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While many Automatic Speech Recognition applications employ powerful computers to handle the complex recognition algorithms,there is a clear demand for effective solutions on embedded platforms.Digital Signal Processing (DSP) is one of the most commonly used hardware platform that provides good development flexibility and requires relatively short application development cycle.DSP techniques have been at the heart of progress in Speech Processing during the last 25years.Simultaneously speech processing has been an important catalyst for the development of DSP theory and practice.Today DSP methods are used in speech analysis,synthesis,coding, recognition,enhancement as well as voice modification,speaker recognition,language identification.Speech recognition is generally computationally-intensive task and includes many of digital signal processing algorithms. In real-time and real environment speech recognisers applications, it's often necessary to use embedded resource-limited hardware. Less memory, clock frequency, space and cost related to common architecture PC (x86), must be balanced by more effective computation. Keywords-Automatic speech recognition,Digital signal processing,Mel frequency cepstral coefficient |
