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In 1969, John Pierce, a researcher for Bell Labs, declared that it was “not easy to see a practical, economically sound application for speech recognition…” (Reddy 6). He discouraged further investigation of this new advancement because it required artificial intelligence, something that certainly would not progress very far. Fortunately, the speech recognition research industry eventually recovered from this discouraging event and expanded tremendously. New developments began to appear rapidly, changing many important aspects of life for various groups of people. Speech and voice recognition technology has significantly impacted society because of its versatility, adding a new level of convenience and accessibility to the modern world. According to Oxford University’s online dictionary, speech recognition is “the ability of a computer to recognize and respond to the sounds produced in human speech” (“Speech Recognition” par. 1). This means that a person can speak commands that will then be carried-out by the computer. Voice recognition is defined as “computer analysis of the human voice, especially for the purposes of interpreting words and phrases or identifying an individual voice” (“Voice Recognition” par. 1). This definition is very straightforward; voice recognition describes the ability of a computer to recognize a person who is speaking and decipher what that person is saying. These terms are often used interchangeably, so it is important to clarify their distinction. It is also essential to note that the use of “computer” in these definitions refers to the core machinery of various devices, not only familiar desktop and laptop computers. Once an individual gains an understanding of these key concepts, a brief history of ... ... middle of paper ... ...e. IDG Consumer & SMB, 2 Nov. 2011. Web. 16 Mar. 2014. Reddy, D. R., comp. Speech Recognition: Invited Papers Presented at the 1947 IEEE Symposium. New York: Academic, 1975. Google Books. Google. Web. 5 Mar. 2014. Schutte, John. "Researchers Fine-tune F-35 Pilot-aircraft Speech System." The Official Website of the U.S. Airforce. U.S. Airforce, 15 Oct. 2007. Web. 17 Mar. 2014. "Speech Recognition." Oxford Dictionaries. Oxford University Press, n.d. Web. 16 Mar. 2014. "The History of GPS." National Parks Service. U.S. Department of the Interior, n.d. Web. 20 Mar. 2014. "Voice Recognition." Oxford Dictionaries. Oxford University Press, n.d. Web. 16 Mar. 2014. Xuedong, Huang, James Baker, and Raj Reddy. "A Historical Perspective Of Speech Recognition." Communications Of The ACM 57.1 (2014): 94-103. Applied Science & Technology Full Text (H.W. Wilson). Web. 12 Mar. 2014.
One of the best-known and interesting findings in speech perception research is the “phonemic restoration phenomenon”. It is a beneficial and amazingly utilized human ability by which, “under certain conditions, sounds actually missing from a speech signal can be synthesized by the brain and clearly heard”(Kashino, 2006. P.318). This shows the brains sophisticated ability in comprehending speech in the everyday life noisy settings.
Seikel, J. A., King, D. W., & Drumright, D. G. (2010). 12. Anatomy & physiology for speech,
Bill Strickland spends his days helping people through Manchester Bidwell. He founded job training programs and also a community arts program to help and mentor young people. When Bill Strickland was younger he did not have the tools and everything he gives to the kids now for mentoring. Strickland’s life changed when he found pottery. It was something he was good at from the start. Bill grew up in Pittsburgh, and it was not the prettiest. People were losing their jobs and the town was falling apart. Strickland’s mother shaped him to be a successful man. She did not let him “fall into the ghettos trapdoor”. Strickland spends his life trying to fix the substandard neighborhood that he grew up in.
States. National Park Service. (2014, May, 12). History & Culture. National Parks Service. Retrieved May 18, 2014, from http://www.nps.gov/yose/historyculture/index.htm
The American public has had a craving for less social contact as the millennia continues to wane, and Siri-Speech is the perfect solution for this need. The average adolescent American sends approximately 88 text messages per day, which is decent but still requires improvement, as they still have to drudge through the burden that is sounds uttered with vocal cords. Although speech has been less arduous in the modern era, with the clever use of acronyms like LOL, TTYL and ILY, there are many other tedious phrases that still need to be sounded out every single day. Siri-Speech addresses this problem as well by converting every single phrase into an Acronym to heighten convenience for the user, so that they can get back to important measures like browsing videos of funny cats on YouTube. For example, a phrase previously spoken as “I have to go. I will see you tonight at the movie theatre” is now spoken as “I have to go,” which is truly the epitome of efficiency and progre...
Lachs, L., Pisoni, D., & Kirk, K. (2001). Use of audiovisual information in speech perception by
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.
Here, instead of storing individual phoneme sounds and mapping them to the phonemes found in the text, parametric models for phonemes in different contexts are saved. The simplest way to describe statistical parametric speech synthesis would be something like this: it generates the average of some set of similarly sounding speech segments. [7]
Abstract—: Stuttering can be defined as speech with involuntary disruption, specially initial consonants. This paper focuses on MFCC (Mel Frequency Cepstral Coefficients) and different methods such as spectrogram analysis and speech waveform for stutter speech analysis. We use Cepstrum analysis to distinguish between a normal person’s speech and that of a stuttering subject. The database is recorded without noise to improve clarity and accuracy in determining Mel Frequency Cepstral Coefficients. 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.
Hearing loss is often overlooked because our hearing is an invisible sense that is always expected to be in action. Yet, there are people everywhere that suffer from the effects of hearing loss. It is important to study and understand all aspects of the many different types and reasons for hearing loss. The loss of this particular sense can be socially debilitating. It can affect the communication skills of the person, not only in receiving information, but also in giving the correct response. This paper focuses primarily on hearing loss in the elderly. One thing that affects older individuals' communication is the difficulty they often experience when recognizing time compressed speech. Time compressed speech involves fast and unclear conversational speech. Many older listeners can detect the sound of the speech being spoken, but it is still unclear (Pichora-Fuller, 2000). In order to help with diagnosis and rehabilitation, we need to understand why speech is unclear even when it is audible. The answer to that question would also help in the development of hearing aids and other communication devices. Also, as we come to understand the reasoning behind this question and as we become more knowledgeable about what older adults can and cannot hear, we can better accommodate them in our day to day interactions.
TRACE I deals with the problems in recognizing phonemes from real speech by identifying phonemes as a function of
Artificial intelligence system for speech recognition is the science and engineering of making intelligent machines, especially intelligent computer programs. Some of its applications are game playing, speech recognition, understanding natural language, computer vision, expert systems, robotics etc. It involves two basic ideas. First, it involves studying the thought processes of human beings. Second, it deals with representing those processes via machines (like computers, robots, etc.).
to modify an assigned baseline duration. In another approach large speech corpora are first analyzed by varying a number of possible control factors simultaneously to obtain duration models, such as an additive duration model by Kaiki [38], CARTs by Riley [3] and neural networks by Campbell [39]. The CARTs (classification and regression trees) proposed by Riley are data-driven models constructed automatically with the capability of self-configuration. The CART algorithm sorts instances in the learning data using binary yes/no questions about the attributes that the instances have. Starting at a root node, the CART algorithm builds a tree structure, selecting the best attribute and question to be asked at each node, in the process. The selection is based on what attribute and question will divide the learning data to give the
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. An alternative theory was developed based on evidence of categorical perception of phonemes: Liberman’s Motor Theory of Speech Perception rests on the postulation that speech sounds are recognised through identification of how the sounds are produced. He proposed that as well as a general auditory processing module there is a separate module for speech recognition, which makes use of an internal model of articulatory gestures. However, while this theory initially appeared to account for some of the features of speech perception, it has since been subject to major criticism, and other models have been put forward, such as Massaro’s fuzzy logic model of perception.
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