Quality Speech Dataset To Avoid Errors

With the launch of voice-activated devices every week, it's easy to believe that we're at an uncharted territory in the technology for speech recognition. Yet, a recent Bloomberg article states that even though voice recognition technology has taken huge advances in recent times but the way it is implemented to the process of Speech Data Collection has hindered it from reaching a level where it can replace how the majority of consumers communicate with devices. People have taken to the idea of devices that can be activated by voice with enthusiasm, however the actual experience is still the potential to be improved. What's holding technology behind?

More data = better performance

The authors say that what's required to enhance the ability of devices to comprehend and interact the user is Terabytes humans' speech information comprising different accents, languages and dialects, to enhance the capabilities of conversational understanding that the gadgets have.

Recent advancements in speech engines are due to a form of artificial intelligence known as neural networks that learn and evolve with time without precisely programmed. In a loose way, they are modeled after our brains and brains computer systems are able to train themselves to understand the world around us, and perform better when flooded with data. Andrew Ng, Baidu's chief scientist, states "The more information we put into our systems, the more efficient it is. This is the reason why speech is a capital-intensive process; not many of companies have this amount of information ."

It's all about quality and quantity.

While the amount of data is crucial but the quality of data is essential to improve the machine-learning algorithms. "Quality" in this instance is how well the data is suited to the application. For instance, if the voice recognition system is developed for use in cars then the data must be taken from a vehicle to achieve the best results. This takes into consideration all usual background noises that the engine will 'hear'.

While it's tempting using off-the-shelf data, or to collect data using random methods, it's more effective long-term to gather AI Training Dataset specifically for the purpose it is intended to be used.

The same principle applies to developing global speech recognition applications. Human language data is nuanced inflected, and full with cultural bias. Data collection needs to be done in a variety of languages, geographic accents and locations to decrease errors and boost performance.

What happens when Speech Recognition Goes Wrong

Speech recognition that is automatic (ASR) is something that we use each day at GTS. Accuracy in speech recognition is something we take pride on helping clients achieve and we're confident that those efforts are admired across the globe as people are using speech recognition on their smartphones as well as on their laptops, or in their homes. Personal assistants from digital technology are in our reach and being asked to set reminders, answer messages or emails, and even to look up us and suggest a place to take us for a meal.

It's all very well however, even the top voice recognition or speech recognition technology has trouble in achieving 100 percent accuracy. If problems occur and the mistakes could be very obvious, even if they are amusing.

1.What kind of errors can occur?

A device that recognizes speech is almost always able to create an array of words depending on the information it has received and that's exactly what they're built to accomplish. However, choosing which word the device heard was a difficult task and there are a couple of things that could make users feel confused.

2.Making the wrong assumption about the word

This is of course the main issue. Natural language software cannot create complete plausible sentences. There are a myriad of possible misinterpretations that could appear similar, but do not create a lot of sense as a full sentence:

3.Listening to things that aren't the words you were using

If you pass someone and you're speaking in a loud voice or you cough during a phrase it's unlikely that a computer is likely to determine which part of the audio you were speaking and which originated from a different portion in the sound. This could lead to situations like a person's phone taking dictate while they were practicing using the tuba.

4.What's happening there?

What is the reason these well-trained algorithms making mistakes that any normal person would find hilarious?

5.What can people do when things fail?

When something goes wrong in the accuracy of speech recognition the chances are that they will remain in the wrong direction. The general public is cautious when speaking to a virtual person even at the best of times . it's not difficult to undermine that trust! Once an error is made individuals do all kinds of bizarre things to be more clear.

Certain people slow down. Some people might over-pronounce their words and ensure that the Ts and Ks are as clear as they possible. Others will attempt to mimic the accent they think computers will most easily understand, and do their best imitation or Queen Elizabeth II or of Ira Glass.

But here's the problem Although these techniques may assist if you're talking to someone who is confused or an individual on a poor telephone line, they won't aid computers in any way! In reality, the more we go from natural spoken speech (the kind found in the recordings that trained the recogniser) and the more complicated the situation will become, and the downward spiral will continue.