Today find widespread application in tasks that require human machine interface, such as automatic call processing in telephone networks, and query based information systems that provide updated travel information, stock price quotations, weather reports, Data entry, voice dictation, access to information: travel, banking, Commands, Avoinics, Automobile portal, speech transcription, Handicapped people (blind people) supermarket, railway reservations etc. For reasons ranging from technological curiosity about the mechanisms for mechanical realization of human speech capabilities to desire to automate simple tasks which necessitates human machine interactions and research in automatic speech recognition by machines has attracted a great deal of attention for sixty years.Based on major advances in statistical modeling of speech, automatic speech recognition systems Speech is the primary means of communication between humans. The main goal of speech recognition area is to develop techniques and systems for speech input to machine. Speech recognition technology has made it possible for computer to follow human voice commands and understand human languages. Speech is the most natural form of human communication and speech processing has been one of the most exciting areas of the signal processing. Speech Recognition (is also known as Automatic Speech Recognition (ASR), or computer speech recognition) is the process of converting a speech signal to a sequence of words, by means of an algorithm implemented as a computer program. Keywords- Speech Recognition, MFCC, ANN, Basic Model, Approach, Application. The paper describes speech recognition technology for The Mel-frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN) and its basic model, approach, application and reviewed the classification of speech recognition systems and voice recognition technology.
The speech recognition technology is gradually becoming the key technology of the IT man machine interface. Speech recognition involves many fields of physiology, psychology, linguistics, computer science and signal processing, and is even related to the persons body language, and its ultimate goal is to achieve natural language communication between man and machine. Patil Institute of Engineering & Technology,ĪbstractSpeech recognition allows the machine to turn the speech signal into text or commands through the process of identification and understanding, and also makes the function of natural voice communication. #Department of Electronics and Communication, Overview: Speech Recognition Technology, Mel- frequency Cepstral Coefficients (MFCC), Artificial
The model uses a synthesis filter to estimate the log spectrum including both zeros and poles in the transfer function, along with the mixed excitation technique which could divide speech signals into multiple frequency bands to better approximate natural speech production.Overview: Speech Recognition Technology, Mel- frequency Cepstral Coefficients (MFCC), Artificial Neural Network (ANN) Compared to regular linear predictive coding (LPC) coefficient which is also widely used in speech synthesis, the mel-cepstral coefficient could resemble the human voice more closely by providing the synthesized speech with more details in the low frequency band. Mel-cepstral coefficients could be used to generate natural sounding voice and reduce the artificial effect. The synthesis model takes mel-cepstral coefficients and spectrum envelopes as features of the original speech waveform. This thesis presents a method to improve quality of synthesized speech by reducing the vocoded effect. Speech synthesis using Mel-Cepstral coefficient feature
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