Artificial Intelligence For Speech Recognition

A text to speech (TTS) synthesizer is a computer based system that can read text aloud automatically, regardless of whether the text is introduced by a computer input stream or a scanned input submitted to an Optical character recognition (OCR) engine. A speech synthesizer can be implemented by both hardware and software. It has been made a very fast improvement in this field over the couple of decades and lot

As the speaker continues more information is given. The listener is then able to collect the verbal and non-verbal data to gain a better understanding of what is being communicated (Eunson 2008: 310).

Dragon Dictate Single, Kurzweli Voice Pad Pro and IBM Voice Type Simply Speaking are some of the speech recognition applications which are currently used by soldiers and defense sector.

JLG Technologies, provides software for correctional investigators across the country. Recently, they released a new software that should make a big impact in the correctional software industry. The name of the software is Investigator Pro 4.0 and the software makes it extremely easy to search by voice. Certainly, correctional investigators spend a lot of time going through phone calls trying to quickly identify subjects on the line through various methods. Those methods might seem slow compared to the powerful new tool that JLG Technologies released. Now, the correctional investigators simply take a sample of the voice and a few seconds later, they identify the callers.

Valid questions can be articulated into the telephone receiver identified as “Siri” and “she” can answer virtually each and every inquiry. Siri is a built-in “intelligent assistant” that enables users of Apple iPhone 4S and later and newer iPad and and iPod Touch devices to speak natural language voice commands in order to operate the mobile device and its apps.

Although the solution to the assignment may sound easy, the creation process was actually very challenging. One reason may be that "Oral speech is fully natural to human beings in the sense that every human being in every culture who is not physiologically or psychologically impaired learns to talk" (Ong, 322). In U.S. culture, oral speech and writing are

When used to estimate the probabilities of a speech feature segment, neural networks allows to discriminative training for a natural and efficient manner. Few assumptions on the statistics of an input features are made with the neural networks. However, in spite of their effectiveness made classifying short-time units such as an individual phones and isolated words, neural networks are rarely successful for an continuous recognition tasks, largely because of their lack of the ability to model of temporal dependencies.

Alternatively referred as speech recognition, voice recognition is a hardware device or a software which has an ability to decode human voice. Voice recognition is commonly used in order to perform command or write without using key board, mouse or pressing any buttons.

While applying an audio-visual approach, pronunciation is stressed, lessons with dialogues are utilized, and mimicry and memorization are used. In a cognitive approach the instruction is often individualized, so students are responsible for their own learning. While using a communicative approach, the goal of language teaching is learner ability to communicate in the target language (Celce-Murcia, 2001). These approaches integrated in software programs support new learning skills more effectively than classical methods for language learning. Beatty (2003) states that the application of CALL might be helpful for learners in improving their reading, writing, listening and speaking skills, as well as encouraging autonomy in learning, and for teachers in improving their teaching skills since it can be integrated into research and

account [2]. These basic features can basically ensure that classification, but there is still much room for improvement. Therefore, some researchers have proposed some new features, such as harmony features [2]. Some researchers also consider in combination acoustic features with other speech features such as semantic features (individual keywords), and facial expression could be a good choice. Dimensionality reduction is indispensable, thus the second issue is further to get optimal features sets with minimal completeness. This stage aims to reduce the size of the extracted speech feature set by selecting the most valuable subset of features and removing the irrelevant ones. Its not easy to give universal and effec-

Voice-based queries have a different nature. They tend to be longer phrases, complete sentences, and questions.Natural LanguageTo optimize your content for the future of SEO, you have to pay attention to how people are searching, because this also changes. People are now “talking” to their devices.

We define {it verbalization} as the process by which an autonomous robot converts its own experience into language. We represent the variations in possible explanations for the same robot experience in the

Mel cepstral feature extraction technique is required in some or the other form in most of the latest speech and speaker recognition system. Here, first samples of speech are splitted into overlapping frames. Generally the length of frame is 25 ms and frame rate is 10 ms. Each and every frames are refined by pre-emphasis filter which amplifies higher frequencies. Next is to apply windowing so that Fourier spectrum for each windowing frame is achieved here Hamming window is used. To obtain vector of log energy, Mel spaced bank of filter is used which varies from 20 to 40 depending upon the requirement of particular application. The output of which is converted to give cepstral coefficients using DCT. Initially for computing the feature vector, 12 coefficients are obtained along with one energy make its 13 cepstral coefficient.

a) A good human computer interface that can translate from a natural language into a computer language and vice versa. A natural language system can act as an interface to a database system. This technology is very useful for the blind people; that with the help of speech recognition interact with computers.

In a quiet room in Universiti Brunei Darussalam, the speaker was recorded using a voice recorder performing the task. Prior to recording, I was given few minutes to read the passage. Then, I was asked to analyze my own recording based on my auditory judgements and acoustic measurements. The auditory analysis is categorised into 5 features (See table 1) and the acoustic analysis is classified into 2 features (See table 2). The auditory analysis is carried out by listening to the sounds and the result is based on my final auditory judgement and visual examination of the