They are HMM (Hidden Markov Model), DTW (Dynamic Time Warping), ANN (Artificial Neural Network) etc. In this paper I describe about a text-dependent speaker recognition using Artificial Neural Network and its propose a method to improve the accuracy of the speaker recognition system. Also hardware implementation of the speaker recognition system is done. In most of the today’s literatures are limited to the study of improvement of accuracy of speaker recognition system in the feature matching stage. The main disadvantage of this method is that it is very complex.
This science is based on a study of all the parts of the body concerned in making speech. It includes the positions of the parts of the body necessary for producing spoken workds, and the effect of air from the lungs as it passes through the larynx, pharynx, vocal cords, nasal passages and mouth. Phonetics sounds (phones) are actual speech sounds classified by the manner and place of articulation (that is by the way in which air is forced through the mouth and shaped by the tongue, teeth, palate, lips and in some languages by the uvula. The [r] of run and far are phonetically different because they are articulated differently. A phonetic system must indicate whether a vowel sound is long or short, runded, diphthongal (that is consiss of two sounds) or retroflex (made with the tip of the tongue curled up toward the palate).
The second approach, called as a linear approach considers an F0 contour as a linear succession of tones. An example of the linear approach to pitch modeling is the Pierrehumbert or ToBI model that describes a pitch contour in terms of the pitch accents . Pitch accents occur at stressed syllables and form characteristic patterns in the pitch contour. The ToBI model for English uses five pitch accents obtained by combining two simple tones, high (H) and low (L) in different ways. The model uses a H+L pattern to indicate a fall, a L+H pattern to describes a rise and an asterisk (*) to indicate which tone falls on a stressed syllable.
How do listeners extract the linguistic features of speech sounds from the acoustic signal? Speech sounds can be defined as those that belong to a language and convey meaning. While the distinction of such sounds from other auditory stimuli such as the slamming of a door comes easily, it is not immediately clear why this should be the case. It was initially thought that speech was processed in a phoneme-by-phoneme fashion; however, this theory became discredited due to the development of technology that produces spectrograms of speech. Research using spectrograms in an attempt to identify invariant features of formant frequency patterns for each phoneme have revealed several problems with this theory, including a lack of invariance in phoneme production, assimilation of phonemes, and the segmentation problem.
Introduction : In this chapter we take a close look at two important issues in text-to-speech synthesis, namely, prosody modeling and waveform generation, and present a review of popular techniques for the same. These two steps are important for generation of natural sounding speech. At the perceptual level, naturalness in speech is attributed to certain properties of the speech signal related to audible changes in pitch, loudness and syllabic length, collectively called prosody. Acoustically, these changes correspond to the variations in the fundamental frequency(F0), amplitude and duration of speech units [2, 4]. Prosody is important for speech synthesis because it conveys aspects of meaning and structure that are not implicit in the segmental
Grammatical functions serve a purpose of relating predicational units and arguments to one another. They are assumed as part of the syntactic inventory of every language and could also be known as Grammatical Relations. Though some argue that the term grammatical relation is vague, and grammatical functions are a more specific term, which is a link between function and structure (Falk, 2000). Moreover, LFG is a phrased used to refer to the designations of SUBJect, OBJect, OBJθ, COMP, XCOMP, OBLiqueθ, ADJunct, XADJunct which will be discussed here. Syntactic capacities can be cross- grouped in a few distinctive ways.
Audio mining is a branch of speech processing that is used to search and analyze the content of an audio signal automatically. Keyword spotting (KWS) is an important audio mining technique which searches audio signals for finding the occurrences of given keyword within the input spoken utterance. KWS provides a satisfactory audio mining solution for various tasks like spoken document indexing and retrieval. The research in audio mining has received increasing attention due to the increase in amount of audio content in the Internet, telephone call conversation and other sources. KWS is classified according to the type of input speech file and the method used for spotting.
Other methods like Cosine transform, Wavelet transform and Q-transform are also used. Frequency features can be divided in two sets such as physical features and perceptual features. 1) Auto regression based features: In auto regression analysis a linear predictor finds the value of each sample which is represented by a lin... ... middle of paper ... ...semantic meaning in the context of human auditory perception are called Perceptual Frequency Features. Brightness, Tonality, Loudness, Pitch, Harmonicity are commonly used perceptual frequency features. A signal is composed of both low and high frequencies.
I. Theoretical Background 1- Scope of the Study: Language has many functions in our lives; it is not only a mean of communication, but it is also a mean of giving and getting information. According to James Paul Gee (2005), "language has a magical property: when we speak or write, we design what we have to say to fit the situation in which we are communicating" (P. 10). Discourse can be defined as a continuous piece of language of several sentences which are related to each other in some way to form coherent meaningful unit. It can be either written or spoken.
We also use a spectrogram to show the clear difference between formant peak changes and how to estimate them for speech analysis and applications for disfluencies. These features can be used for enhancing speech recognition techniques such as security systems, call detection and automated identification for people with stuttering. Keywords— Cepstral Analysis, Mel Frequency Cepstral Coefficients, Spectrogram, Stuttering. Introduction