T
HE speech signal conveys many levels of information to the listener. At the primary levels speech conveys an message. But also speech conveys information about the gender, emotion also the identity of the speaker. The goal of the speaker recognition is to extract, characterize the information about a speaker identity from speech signal. There are two types of speaker recognition tasks. Text-dependent speaker recognition and text-independent speaker recognition. In text-dependent speaker recognition, recognition phrases are fixed, whereas in the case of text-independent speaker recognition, the recognition phrases are not fixed
Speaker recognition consists of mainly two steps. They are feature extraction and classification stage. In feature extraction stage, features are extracted from the speech signal. There are many methods for feature extraction. Most widely used methods are MFCC (Mel Frequency Cepstral Coefficient), LPC (Linear Predictive Coding), LPCC (Linear Predictive Cepstral Coefficient) etc. Also there are many methods for feature matching. 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. So to avoid this complexity here I propose a method which improve the accuracy of the system by changing the feature ...
... middle of paper ...
...te is established. In each frame Energy, Most Dominant frequency and Zero Crossing Rate are find out and compare it with a predefined threshold values, and if any 2 of them satisfies the condition, then that frame is marked as speech frame otherwise it is discarded.
B. MFCC
MFCC is based on human hearing perceptions which cannot perceive frequencies over 1Khz. In other words, in MFCC is based on known variation of the human ear’s critical bandwidth with frequency. MFCC has two types of filter which are spaced linearly at low frequency below 1000 Hz and logarithmic spacing above 1000Hz. A subjective pitch is present on Mel Frequency Scale to capture important characteristic of phonetic in speech. The overall process of the MFCC is shown in Figure 3.2.
C. Artificial Neural Network
Here I am using Artificial Neural Network for pattern matching with 2 hidden layers.
Session #1: The speech language pathologist (SLP) modeled and role-played different types of voice tone. According to Jed Baker (2003), when demonstrat...
Seikel, J. A., King, D. W., & Drumright, D. G. (2010). 12. Anatomy & physiology for speech,
Biometric and encryption sounds are not too fresh, for most users, any of each technology alone is not enough to make you feel excitement. However, If we develop a new study or a new technology, which combine the biometrics and encryption. We can find that the technology combining these two techniques are very amazing.
Automatic speech recognition is the most successful and accurate of these applications. It is currently making a use of a technique called “shadowing” or sometimes called “voicewriting.” Rather than have the speaker’s speech directly transcribed by the system, a hearing person whose speech is well-trained to an ASR system repeats the words being spoken.
If a person's hearing or sight is impaired, body language and tone of voice will become more important.
Vocal elements such as pitch, projection, articulation, tone and pace are essential in the assemblance of a successful
Biometrics is described as the use of human physical features to verify identity and has been in use since the beginning of recorded history. Only recently, biometrics has been used in today’s high-tech society for the prevention of identity theft. In this paper, we will be understanding biometrics, exploring the history of biometrics, examples of today’s current technology and where biometrics are expected to go in the future.
Delgado, R & Kobayashi, T 2011. Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop. 1st ed. Springer.
The world, as of the 21st century, is increasingly becoming an interconnected, interrelated social place in which avoidance of human interaction is near impossible. From telephone calls to advertising billboards, communication is ubiquitous. Communication essentially refers to the generation and receiving of messages across a variety of contexts, channels, media, and cultures. This complex interaction is composed of both verbal and non-verbal interactions. Verbal language is defined as the use of sounds and language to communicate a message and thus accents, dialects, and languages all fall under this “verbal code.” Its counterpart, non-verbal language, is communication through a host of nonlinguistic methods, including physical appearance, kinesics, and olfactics.
... of the carbon microphone that make is so distinct from all other electronic sender tones. With the combination of the real sound of a voice passing through the carbon microphone fused with the technology we have today, the model that is created by Oksanen and Valimaki and used by a range of producers “incorporates a filtered noise source to model the self-induced noise generated by the carbon microphones” (Oksanen, Valimaki 27). With the scientific ability our world has today, we are able to recreate a sound that was first discovered over a century ago. The development of the carbon button microphone definitely makes a statement about our ever-changing technological society. Although it was an object invented many years ago during the Second Industrial Revolution, it has been altered and improved and has therefore left an everlasting impact on our society.
The origin and history of simultaneous communication are one of the most interesting phenomenons in the world of communication. This based on the fact that simultaneous communication essentially began as a channel of directing someone to something without the use of a particular language. In this, simultaneous language became widely in the any communication process that entailed more than two languages (Gaiba, 1998). It is realized that modern simultaneous interpretation utilizes sophisticated sound equipment for the enhancement of communication, proving simultaneous interpretations historical antecedents. Simultaneous co...
as Hertz (Hz). The sounds of speech are in the range of 250 Hz to 4000
Lachs, L., Pisoni, D., & Kirk, K. (2001). Use of audiovisual information in speech perception by
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.
The purpose of biometrics is to identify a person based on the measure of his anatomical and physiological characteristics. This concept is not new, its history dates back to 14th century when Chinese used footprints to identify newborns and divorce records. In the 1880s, Henry Faulds, William Herschel and Francis Galton were the first who conceived the idea of using biometric identification to guaranty the security in civil areas, like using footprints for criminal identification and fingerprinting t...