Speech Recognition Dataset: Meaning And Its Quality For AI Models

You've got Siri, Alexa, Cortana, Amazon Echo, or other voice assistants in your daily life, then you'll be able to think with me that voice recognitionhas become a standard feature of our life. The artificial intelligence-powered voice assistants translate user's questions in verbal text, and then analyze and interpret the way the person is talking to give the correct answer.

It is vital to gather high-quality data in order to develop precise speech recognition models. However, designing software to recognize speechis difficult task because recording human voice in all detail , including accent rhythm and pitch, and clarity is a major challenge. In addition to that, when you include emotion into the mix, it can be quite difficult.

What does it mean? Speech Recognition?

The software for speech recognition's capability to recognize and translate humans' spoken words into the form of text. Although the distinction between Speech Data Recognition and voice recognition might be subjective for some, there are some fundamental distinctions between the two.

While both speech and voice recognition are components of technology for voice assistants they are used for two distinct purposes. The process of speech recognition the process of automating the transcription of human commands as well as the speech itself into text. The focus of voice recognition is to recognize voices of speakers.

More data = better performance

The tech giants like Amazon, Apple, Baidu and Microsoft are all working hard to collect information about natural language across the globe to improve the precision in their algorithm. According to Adam Coates from Baidu's AI lab, which is located at Sunnyvale, CA states, "Our goal is to reduce the error rate to a minimum of . This is when you can be confident the fact that Baidu is able to understand the language you're using and that it will completely change your life . "neural networks that can adapt and learn over time, without the need for specific programmers. It is said that in a general sense they're modelled after human brains. These machines are able to comprehend what's happening around them and are more effective when they are bombarded with information. Andrew Ng, Baidu's chief scientist, says "The more information we incorporate into our systems and the more accurate they are, the better they will perform. This is speech is a costly process; however, not all firms have this type of data . "

All it is about the quantity and quality

While the quantity of data is important, the quality of the data is crucial to enhance machines-learning algorithmic. "Quality" in this context refers to the degree to which data is in line with the intended purpose. For example when the system for voice recognition is designed to be used in cars, then the data needs to be taken from a vehicle in order to achieve the most effective result, taking into account all of the background noises that engines will detect.

Although it's tempting to use off-the-shelf inventory data, and even for AI Data Collection using different methods is more efficient in the end to collect specific information for the purpose of its usage.

The problem is what happens when Speech Recognition Goes Wrong

It's all fine however, even the most advanced speech recognition software has a hard to achieve 100 percent precision. When problems arise, the errors can be glaring, even if they're humorous.

1.What kinds of errors could occur?

The device which detects speech, will typically create various words depending on the speech it detects, since it's what they're meant to do. However, selecting the strings of words that the device detected was not an easy task since there are many factors that can cause users to feel confused.

2.Hearing things which don't match your words

If someone is passing by and you're talking in a loud manner or you're coughing half way through a paragraph, it's unlikely that computers are likely to be able to tell which parts of your voice are a distinct part of the audio. This could cause situations like the phone taking a note when they were playing in the tuba.

3.The incorrect word is being used to guess the correct

It is by far the most frequently encountered issue. Natural language software is unable to create fully acceptable phrases. There are many possible interpretations that are similar however, they do not make much sense as a complete sentence:

4.What's going on there?

Why are these expertly educated algorithms making mistakes that anyone would find funny?