The Voice Monitoring And Sampling Software

A ____________________ typically includes a data flow diagram to describe the flow of information for a

• The determination of the processes and data that are required by a new system.

For this report a human-machine system had to be chosen to analyse. The chosen product is a mobile phone, more specifically a Nokia 6220 (as pictured below). This product is relevant to this project because it encompasses many of the different topics that will be considered and when

As the VP Nurse at One Tree Hill medical clinic, I have noticed an increased number of patients over the past 5 years, as well, the progress notes, patients’ information, and the nurse’s notes in the patient files are not clearly legible and appear to be lacking the attention they require. An additional ripple effect has been demonstrated in which our transcriptionist has been requiring an increase in working hours when completing duties. As a result, overtime costs are now being incurred, satisfying the transcriptionist workload. As an administrative support tool, I would like to implement the Dragon Medical voice recognition software into our clinic, as a methodology to alleviate this situation. After extensive research on the available

Review of literature on speech recognition systems authentically demands consideration towards the finding of Alexander Graham Bell regarding the method of converting sound waves into electrical impulses and the first speech recognition system developed by Davis et al. [6] for finding telephone superiority digits spoken at normal speech rate. This attempt for automatic speech recognition was mainly centered on the edifice of an electronic circuit for discovering ten digits of telephone superiority. Spoken words were examined to obtain a 2-D plot of formant 1 vs. formant 2. A circuit is developed for finding the greatest correlation coefficient among a set of novel incoming data for pattern matching. These features are grouped into feature vectors that serve the purpose of reducing dimensionality and redundancy in the input to the speaker recognition system. An indication circuit was created to exhibit the already discovered spoken digit. The proposed approach lays stress on recognizing speech sounds and providing appropriate labels to these sounds. Various approaches and types of speech recognition systems came into existence in last five decades gradually. This evolution has led to a remarkable impact on the development of speech recognition systems for various languages worldwide. The exact nature of the feature set depends on what part of a speech signal the features are expected to represent and thus what type of information is to be extracted. In

This program records the voice 'Get ready to record the voice ' %Read data of letter "a" sound f1=wavread ( 'vc_a ',22050); %Transform the data using Fast Fourier Transform function F1= fft(f1,11025); %Compute to obtain the Normalized Power Spectrum Density of the %transformed data Pf1 = F1. * conj(F1)/11025; Pf1 = transpose(Pf1); %Transpose Pf1 = Pf1(1:2206); %Only the first 2206 sample data needed f2= wavread( 'vc_a2 ',22050)

Android operating system is used in many device such smart phone and tablets. Based on a report android in the fourth quarter of 2013 has a market share of 78.1% of all smartphone. [3] Furthermore, with the

In order to obtain relevant speaker features, the vectors are analyzed towards characteristic factors. Thereby, a factor analysis model is iteratively trained. The factor analyzed features are referred to as identity-vectors (i-vectors).To obtain the i-vectors, first a speech Universal Background Model (UBM) is trained on a training data. The UBM is a GMM with large number of Gaussians, so that it captures all possible variabilities in speech in the feature space. In the proposed system the TIMIT and TIFR datasets have been used for the UBM training.

During the build phase, the requirements are used by the IT technical staff to construct the system. The goal of

First, auditory learners would rather listen to things being explained than read about them. Reciting information out loud and having music in the background may be a common study method. Other noises may become a distraction resulting in a need for a relatively quiet place. There are many technological systems that help aid these types of students including the use of audio books, recording devices, and natural reader systems. Audio books allow the student to listen to the book in order to understand the content to the best of their ability. Also, the teacher and the student can use recording devices. Teachers can record their lectures and distribute the recording to the students in need and students can record notes to use and listen for the future. In addition, natural reader systems are text to speech software with natural sounding voices. This software can convert any written text such as MS Word, Webpage, PDF files, and Emails into spoken words. This can also convert any written text into audio files for the students to listen to in the future. These tools are best for these learners because they help them hear what is being taught (Shannon Hutton, 2013).

The interview session has been done by include an open-ended and closed-ended question which are related to the implemented project. Next, sampling technique is executed by system analyst who does evaluation to the current system or prototype. These processes give feedback in evaluation form that filled after tested the system. Lastly, observation is performed by using questionnaire form. According to Burch (1992), the questionnaire is analyzed and transform into structured form that easy to understand. After all information has been collected, structuring of system requirement takes place. It focused on development process modeling which perform “graphically representing the process, or actions, that capture, manipulate, store, and distribute data between a system and environment” (Hoffer, George, & Valacich, 2012, p. 182). In this step, Data flow diagram (DFD) is structured by system analyst using special tools and techniques to create a decision table. According to Hoffer, George and Valacich (2012), decision table is a “diagram of process logic where the logic is reasonably complicated” (p. 200). This table is useful to help system analyst to make a decision toward the project. Then all information’s gained from this phase are documented in System Analysis Report (SAR) that acts as a guideline or reference to the future system development project (Burch, 1992).

Access to large number of applications developed for the device, including both practical and entertainment applications

Abstract – In the past few years an outstanding growth has endorsed the necessity of videos for the determination of communication. However, non-native diction speakers or people with hearing affliction are unable to take advantage of this robust medium of communication. So subtitles are provided for videos to overcome the problems caused by hearing disabilities or language enclosure. The subtitles generated are stored in the form of a subtitle file most commonly having a .srt extension. There are many softwares available to generate subtitles file manually, however software for automatically generating subtitles are scarce. In this paper, we introduce a system that we have envisioned will generate subtitles automatically through a 3- stage process: Audio extraction, Speech recognition and Subtitle synchronization. Three parts are eminent: The first one includes splitting audio from video and converting the audio in suitable format if necessary. Recognition of speech contained in the audio is second module. Generateing a subtitle file from the recognition results of the previous step is final stage.

Bayes classifier [11] is one of the oldest classification algorithm that works based on the Bayes Theorem. Bayes theorem works based on prior probability values and assumes independence between the features used for analysis. The bayes classification model works efficiently on very large datasets. It is simple and easy to build. Bayes classifier used in emotion detection for speech gives an average accuracy of 67%.

In Data Flow Graph (DFG) representation, the nodes represent computations and the directed edges represent data path and each edge has a non-negative number of delays associated with it.