Quality Speech Dataset To Avoid Mistakes
With the launch of voice-activated devices each week, one could think that we’re nearing an end of the road for application of technology to recognize speech. Yet, a recent Bloomberg article states that even though voice recognition technology has taken significant advancements in recent years however, the method used to the process of Speech Data Collection has hindered it from reaching the point that it could replace the way the majority of consumers communicate with devices. People have taken to the idea of voice-activated devices with enthusiasm, however the actual experience is still potential for improvement. What’s holding technology behind?
More data = better performance
As per the author’s report, what’s required to enhance the ability of devices to comprehend and interact to users are terabytes humans’ speech information that spans different accents, languages and dialects, to enhance the capabilities of conversational understanding that the gadgets have.
Recent advances in speech engine technology are the result of a type of artificial intelligence known as neural networks that learn and evolve in time, without the need for precisely programmed. In a loose way, they are modeled after our brains and brains computer systems can be trained to comprehend the world around us, and perform better when they have more AI Training Data. Andrew Ng, Baidu’s chief scientist, says “The more information we put into our systems, the better they perform. This is the reason why speech is such a costly procedure; not a lot of businesses have this kind of information .”
It’s all about quantity and quality
Although the amount of data is essential but the quality of data is crucial to optimize the machine-learning algorithms. “Quality” in this case is how well the data is suited to the application. For instance, if a voice recognition system is being designed 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 is able to detect.
While it’s tempting using “off-the-shelf” data, or to collect data using random methods, it’s more effective long-term to collect data that is specifically designed for the purpose it is intended to be used.
The same principle applies to creating global speech recognition software. Human speech data is nuanced, inflected, and full of biases based on culture. Data collection needs to be done in a variety of languages, geographical places and accents in order to reduce errors and increase the efficiency.
What happens when Speech Recognition Goes Wrong
Speech recognition that is automatic (ASR) is one of the things we deal with each day at GTS. Accuracy in speech recognition is one of the things we are proud of on helping clients achieve and we’re confident that those efforts are appreciated all over the globe as more people utilize speech recognition on their smartphones or computers or even in the home. Virtual personal assistants are in our reach and are asked to schedule reminders, respond to messages or emails, and even to look up us and recommend a good place to have a bite to eat.
All well and good however even the top voice recognition or speech recognition software has a difficult time in achieving 100 percent accuracy. If problems occur and the mistakes are often glaring or even amusing.
1.What kind of errors can occur?
A device that recognizes speech will usually produce an array of words depending on the information it has received as that’s what they’re made to accomplish. However, deciding on the word that it’s heard can be a challenging task as there are handful of factors that can cause users to be confused.
2.Making the wrong assumption about the word
This is of course the most common problem. Natural language software does not have the ability to form complete plausible sentences. There are a myriad of possibilities of misunderstandings that be similar, but they don’t make much sense as a full sentence:
3.Things that don’t match the words you were using
If someone passes by 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 talking about and which ones were a different part that is playing. It could result in things 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 errors that any human listener would find laughable?
5.What should people do when things fail?
When something goes wrong in the accuracy of speech recognition the chances are that they will continue to go wrong. The general public is nervous when they speak to a virtual person even at even the most tense of times. it’s not hard to undermine that trust! When an error is made individuals do all kinds of bizarre things to clarify themselves.
Some people will slow down. Others may over-exaggerate their speech and ensure that the Ts and Ks are as precise as they could be. Others will attempt to mimic the accent they believe the computer can most easily understand, and do their best imitation of queen Elizabeth II or of Ira Glass.
The thing is While these methods may assist if you’re talking to someone who is confused or people on a poor telephone line, they do not assist computers at all! The further we deviate from the natural spoken speech (the kind found in the recordings that were used to train the recognizer) and the more complicated things will get and the cycle will go on.