In a time where voice-enabled assistants are becoming more commonplace, the future is quickly approaching. Artificial intelligence (AI) has been implementing itself into our lives, creating products we can use it to do things that would normally be impossible for humans. For example, the current AI already has enough intelligence to know how to handle certain requests. If you ask for Google Assistant to play your favorite playlist then it will start playing it. Therefore, it is no surprise that the future of AI’s role in voice interfaces is already beginning to take shape.
Now, let us look at how this is possible. The underlying reason behind the success of this technology can be attributed to: accurate speech data annotation. And in this article, we will explain why that is essential for the application and development of voice recognition technology.
How sound can be analyzed and processed to recognize words and sentences?
When we hear speech, our brain first listens to the sounds, then processes the meaning of what we are hearing. As humans, we have a basic understanding of certain sounds that describe certain objects or concepts. For example, we can pick up on a frequency range for the word "bathroom" simply because it is characteristic of describing a room we go into to take a shower.
This process is called acoustic modeling. A "speaker independent" acoustic model examines the characteristics of a sound, or the acoustic signature for a word. However, in order to be able to learn something from acoustic data, you need accurate annotation.
Accurate Speech Data Annotation
The process of labeling speech data is often referred to as "labeling". It involves extracting meaningful labels or tags from the free text of a speech transcript. These tags can then be used to create acoustic models, which are also called "training data."
Labeling may seem like an easy task, but it is a job that requires a lot of effort. In fact, according to research, about 40% of the total training hours for natural language systems is spent on labeling. Furthermore, each speaker is only labeled once.
In the past few years, there have been a lot of attempts made to try and make the process of labeling speech data as quick and efficient as possible. These approaches include:
The most common approach involves simply feeding a text transcript into an automatic speech recognition (ASR) system.
The second approach involves using a large number of speakers to create a set of labeled data for each transcript. Here, you need to count the number of times that each word occurs in the text transcript. In cases where the word is not used in any part of the speech, it is called "free text."
A third approach involves using a method called "continuous speech recognition". In this approach, each word that is recorded has to be broken down into its individual syllables or phonemes. This is a difficult problem, and it is typically solved using machine learning techniques.
As the technology continues to advance, we can expect voice recognition systems to become more intelligent and functional. In the near future, we might have personal assistants that can easily schedule appointments or even make phone calls for you. They might be able to control your home or office equipment. Voice assistants might even be able to understand more complex sentences, like "Where do you want to go for lunch today?"
However, as much as we would like to think that all these possibilities are possible, it is important to remember that it is difficult and time-consuming to create accurate speech data. Nonetheless, we are on the right track. And as long as we continue to feed more and more accurate speech data into our machines, we will have a greater chance of successfully optimizing our AI research and development. And that is why accurate speech data annotation is so essential for the application and development of voice recognition technology.
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