Voice recognition is normally used to operate a device i.e. computer or user made device, perform commands, and operate without keyboard, mouse, or by pressing any buttons. Today, this is implemented on a computer with built-in or separately created automatic speech recognition (ASR) software. Many speech recognition programs require the user to train the program to recognize their voice so that the signals provided in form of human voice can stored more accurately. For example, you could ask like "open chrome" and the computer would open Google
Demerits • Still the best speech recognition applications sometimes make errors. They raise noise or some other sound the number of errors will increase. • Speech recognition works best if the microphone is close to the user.More distant microphones like on table, wall will tend to increase the number of errors. • Speaker
It will also implement signs that can be converted to speech. Thus the application will solve the problem of communication by letting the speech impaired people make sings in front of a web cam and thus produces a resultant voice output. Sign recognition is a typical applies of image understanding as it involves capturing, detecting and recognizing of hand signs. A functioning sign language recognition system could provide an opportunity for the speech impaired to communicate with non-signing people without the need for an interpreter. It could be used to generate speech or text making the speech impaired more independent .
This has introduced a relatively recent research field, namely speech emotion recognition, which is defined as extracting the emotional state of a speaker from his or her speech. It is believed that speech emotion recognition can be used to extract useful semantics from speech, and hence, improves the
When fluent readers read silently, they recognize words automatically. They group words quickly to help them gain meaning from what they read. Fluent readers read aloud effortlessly and with expression. Their reading sound natural, as if they are speaking. Readers read aloud effortlessly and with expression.
Speaker identification is the process of determining which registered speaker provides a given utterance. Each block of the speaker recognition system can be described as below; • Input Speech: Input speech is the signal given by the speaker to the above system. Normally the human speech is the pure analogue signal, thus in order to process the signal further the analogue signal has to be converted to the digital signals. This conversion can be done by using the techniques such as sampling and quantization which is known as digital signal processing. But for the above system, the input speech signal is already given in the digital form which can be done by recording the voices of the speaker.
In speech recognition, feature extraction is the most important phase. It is considered as the heart of the system .The work of this is to extract those features from the input speech that help the system in identifying the speech. Main objective of this paper is analysis and summarize mostly widely used feature extraction techniques like Linear predictive coding(LPC).Linear predictive cepstral coefficient (LPCC),Perceptual linear prediction (PLP) and Mel frequency
The purpose of processing speech signals is to enhance and extract information, which is helpful in providing as much knowledge as possible about the signal’s structure i.e., about the way in which information is encoded in the signal. 2.1.1 The mechanism of speech production Human speech production requires three elements – a power source, a sound source and sound modifiers.
Hence this system converts the sign language into an understandable form for the normal people and detects the hand motions in few seconds. It has got an accuracy of 99%. fig 3: Hardware setup of the system. V. FUTURE ENHANCEMENT The overall system recognises only a partial sign language in which the gestures are based on the finger movements. Facial expressions is also a part of communication.The future work can be extended to recognise more words and also to capture the facial expressions by employing more flex sensors and MEMS