Speech recognition is the act of a computer listening to what you are saying and converting it to written text. This may seem like a very simple task to do, knowing that computers are astonishingly fast and powerful but this is quite the contrary. Most recognition software can achieve between 98% to 99% accuracy if operated under optimal conditions. Optimal conditions assume that users have speech characteristics which match the training data, can achieve proper speaker adaptation, and work in a clean noise environment (e.g. quiet office or laboratory space). The two essential steps that a speech recognition system must accomplish are training and decoding. There are two classes of speech recognition, one called speaker independent, which has a small vocabulary of words/commands, and the other called speaker dependent, which has a very large vocabulary but must be trained for each and every user. This training step might involve a user reading a book aloud to the computer, while the system is following along to the words that are being enunciated. It can also involve the input of prerecorded speech and transcribing the audio to the corresponding text word. The speaker independent system's training involves the collecting of different commands and configuring them for different accents and for the differences in the male and female voice, slang, acronyms, articulation in the words, and temporal non-uniformity. An intriguing hurdle that speech recognition must overcome is homonyms, which are words that have sound the same but have different meanings. The common solution to this problem is understanding the context the possible words will be used and picking the corresponding word. This solution can also be used in all forms o... ... middle of paper ... ...of the voice. One recent application of voice recognition technology in entertainment is the horror movie Last Call. When viewers buy their tickets they are asked to provide their cell phone number. Before the movie starts the database of phone numbers for the movie showing are sent to to the company. Sometime, during the movie, an audience member’s cellular phone will ring, and it is up to this audience member to give the character on screen directions. Astonishingly the movie is controlled by a random viewers voice. Also the software has to overcome the loud background noise of the movie. Voice recognition have even reached the video game market. Their defining feature is that the player controls the game entirely by using a microphone to speak commands to the on-screen characters. the commands are interpreted by the in-game voice recognition software.
Imagine living during the 1960’s when the nation was divided by segregation. The only way to express your ideas, beliefs, and thoughts during that time was through words. Famous Civil Rights activists such as, Dr.Martin Luther King Jr., inspired many with his wise words and empowering speeches. Times when many felt unheard or invisible, words were there as tranquilness and an ataraxia. Words have the power to provoke, calm, or inspire by motivating others to take action in what they believe in.
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...
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.
The article begins by defining phonemic awareness and gives examples which gave me a better grasp of the topic. Phonemic awareness gives a person the ability to hear the difference between sounds in words even when they sound similar such as the words “sat” and “sit”. Phonemic awareness is a division of the larg...
The Phonological Components Analysis treatment program is an Internet-based program designed to improve naming deficits in individuals with aphasia. This audio program was accessed remotely by both the participant and the clinician. The clinician presented pictures and target words on the computer screen which included a question for the participant to answer, such as “What sound does this picture end with?” Then results for the three usability elements were
Here, actually, speech is decomposed into parameters like acoustic features such as fundamental frequency, the shape of the waveform, aperiodic energy etc and duration features related to contextual prosody. And the text is decomposed into various linguistic information. Then Hidden Markov Model or Deep Neural Networks can be used who will learn how to predict parameters such as acoustic features and duration features from the linguistic information of text data during the training phase. [8]
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.
Artificial intelligence has been portrayed as everything from a controlling computer mind, to a devious android type creature pent on destructive termination of human live forms. In reality, it is a science with limitless potential to advance mankind. Various software applications help businesses and there are AI systems that can perform an assortment of tasks interpreting voice
Phonemic Awareness is very important part of literacy. Phonemic awareness includes sounds of a word, the breakdown of words into sounds. It includes rhyming and alliteration, isolation, counting words in sentences, syllables and phonemes, blending words, segmenting, and manipulating.
In addition to its lack of cognitive ability, a computer can not form expectations based upon a situation. A political speech can be very serious, but when seen on Saturday Night Live, it will be interpreted as funny. A similar problem is the extra meaning conveyed by the tone of voice or body language. We could always program an exhaustive data bank with all the different possibilities of input, but the system as we know it could not search all of these within a reasonable period of time, nor could it adapt to future changes. One of the problems with natural language processing systems is that humans themselves are often not very good natural language generators or processors. We often apply our own bias and expectations to what we hear. Two people can make eye contact and set up a whole series of background such that they now what each other are talking about. That very fact may be a bonus for computer natural language processing because we can predict with certainty how the system will interpret the information and therefore have greater clarity than people.
One of the main benefits of speech recognition system is that it lets user do other works simultaneously. The user can concentrate on observation and manual operations, and still control
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.
than one way. Natural languages are ambiguous, so computers are not able to understand language the way people do.
Delgado, R & Kobayashi, T 2011. Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop. 1st ed. Springer.
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.