The term ‘Data Source’ refers to any format from which data is obtained for a Speech to text datasets. Information sources can be various things, such as speech recognition audio clips or live webcams. They can also be relational databases like encyclopedias, thesauruses. In some cases, the speech recognition source can be a subset of another data source.

Image Positioning System

Let’s assume that a company is developing a speech recognition software called the Image Positioning System (IPRS). Their database contains images of products that have been used for marketing purposes. Their database also includes the names and contact details of sales personnel. Their speech recognition software can be used to detect similarities in the images of the product to the speech recognition database and extract call details. However, what if the company’s database contains speech recognition audio clips that clients have submitted?

Analyze Data

The same question can be asked as to which data is used for the voice recognition system. What if the company wants to analyze data derived from social media sites like Facebook? Or what if the company needs to analyze data obtained from medical transcription services? The answer to all these questions lies in the notion of source and ‘targeted’ data.

Data Source

Data source refers to the database from which the speech recognition software extracts information. It may be general unstructured data, or it may be structured in a specific way. Data harvesting is an integral part of data collection. It involves collecting data for research purposes. The data may be used for training, to improve the functioning of a particular business or for any other reason. In all such cases, data mining plays an important role.

Structured &Logical Form

However, data on which the voice recognition system is based must be available in structured and logical form. Otherwise, no data mining can take place. Similarly, the database format must also be consistent so that the software can make sense out of it. Finally, to understand which data is used for the voice recognition system, it is necessary to know it. The data includes recordings of various voices, dictations, interviews etc. Thus, there are four sources from which the information is gathered.

General Unstructured Data

Source one is general unstructured data. This kind of data is generally not structured in any way. For example, there is no specific pattern to the words or even the tone of voice. Sometimes, the data is qualitative, sometimes quantitative. For instance, if a customer has given his opinion about a particular product, this opinion is captured in a text document along with the product name and product specifications. This data may be used in a voice recognition project.

Also read: What is included in a business analysis?

Labelled Conditioned

The second kind of data, which is generally used for which data is used for voice recognition systems, is labelled ‘conditioned’. This data is usually used to train artificial intelligence. Today, the data used here comprises texts, speeches etc., which have been rated in different categories and then are given a score based on their relevance.

Last Words:

Finally, data of actual time usage is another category of data used for which voice recognition system is used. Here, the user’s voice is analyzed, after which a speech recognition program is developed to generate a list of possible future spoken conversations. In addition, the user’s typing data, stored in a database, is also extracted and used in this process. With such a rich data source, voice recognition software can filter unimportant data and generate a high-quality voice recognition result. Hence, one can imagine how useful this technology can be.

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