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.
For a computer to interpret a signal, it must possess a set of vocabulary or words to compare with. The speech patterns are first to be stored on the hard drive and loaded into memory when the program is run. A comparator checks these stored patterns of signal against the output of the A/D converter. All voice-recognition systems or programs make errors. Noise in
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 .
Speech is a primary mode of communication between human being and is also the most natural and efficient form of exchanging information among human beings. Speech Recognition is a conversion of an acoustic waveform to text. Speech can be isolated, connected and continuous type. The goal of this work is to recognize a Continuous Speech using Mel Frequency Cepstrum Coefficients (MFCC) to extract the features of Speech signal, Hidden Markov Models (HMM) for pattern recognition and Viterbi Decoder for decoding of speech signal. Continuous Speech files of the TIMIT standard database are used for the work.
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
Chapter 2 Human Speech Production and Perception 2.1 Human Speech Production Speech signals are composed of a sequence of sounds. These sounds and the transition between them serve as a symbolic representation of information. The arrangement of sounds (symbols) is governed by the rules of the language. The study of the rules and classification of speech is called phonetics. 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.
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
Introduction: Speech is traditionally thought of an exclusively auditory percept. However, when the face of the speaker is visible, information contained primarily in the movement of the lips contributes powerfully to our perception of speech. This combined interaction between auditory and visual modalities improves our ability to interpret speech accurately; particularly in low signal to noise ratio (Bertelson, 2003).This multisensory integration provides a natural and important means for communication. The benefit of integrating audio visual cues has been well documented in normally hearing individuals especially in difficult listening conditions and for listeners with hearing impairment (Sumby& Pollack, 1954). The benefit derived from speech reading can be substantial allowing unintelligible speech to become comprehensive, or even exceeding the benefit derived from the use of assistive listening devices, counseling or training especially those with hearing impairment (Walden et al, 1981).
Here we organize this report into the following categories: speech content editing, facial expression analysis and modeling, facial motion retargeting, and head motion synthesis. Speech motion synthesis Speech motion synthesis, which is also known as lip-synching, refers to combining facial motion that is synchronized with input speech. Most approaches mark the input speech signal based on standard speech units, such as phoneme. This can be manually or automatically. These speech units are then mapped to a set of lip poses, called Visemes .
ABSTRACT In speech recognition, speaker-dependence of a speech recognition system comes from the speech feature, and the variation of vocal tract shape is the major source of inter-speaker variations of the speech feature. Speaker normalization is a process to transform the short-time speech feature of a given speaker to better match some speaker independent model. Vocal tract length normalization (VTLN) is a popular speaker normalization scheme wherein the frequency of the short-time spectrum associated with a speech is rescaled or warped to normalize the speech. In this work, we develop a speaker normalization scheme by exploiting the fact that frequency domain transformations can be accomplished entirely in the cepstral domain through the