Speech recognition refers to the computational ability to convert spoken language into text. Speaking is our most natural way of communicating, and it is also our fastest way to do so. While increased speed is the main reason voice command is rapidly becoming a main feature on smart devices, it’s also a matter of convenience. Practicality has long been known to boost user uptake, especially in relation to technology.
That said, speech recognition technology is still not perfect due to the infinite diversity in speaking style. Despite this branch of Artificial Intelligence (AI) having been around since the late 1950s, the high level of nuance in human speech is keeping computers from obtaining complete accuracy.
Natural Language Processing (NLP) is the field within AI that is tackling this challenge by developing effective applications that allow for the interaction with machines using natural language. The research of this ever-expanding field is key to improving speech recognition and its training as a means to increase the precision we can achieve with the technology. Speech recognition training has a beneficial effect on machine learning.
Speech Recognition Training Feeds Machine Learning
As with most AI streams, training a smart technology involves feeding the software with relevant high quality datasets. The more datasets a machine runs through, the greater experience it gains and the stronger the algorithm it builds. In terms of speech, the more practice a computer can get, the faster it will get at processing data due to its familiarity with the type of data.
However, accuracy is not something machines can improve without any human input or the use of quality datasets. This is where annotated data comes in. Making the most of the linguistic data online generated by the billions of internet users today means channelling it into machine learning tasks. Our scope would be limited to much smaller amounts of data and an even longer turnaround time if we were left to do this work manually.
Annotated Data In a Nutshell
Annotating data is the act of labeling digital information in a form that can be indexed when processed by machine learning. These labels provide further value to the significance of data by adding attribute tags. Given that data annotation is mainly overseen by human analysts, great accuracy is required during this tagging process to ensure that the computer algorithms learn as effectively as they can efficiently. Relevancy to the task at hand is another important factor in achieving the desired outcome so as to avoid any irrelevant data patterns from being learnt.
The Main Types of Data Annotation
The application of annotated data is increasingly versatile which implies that its uses are also expanding. Here are some of the different types of data annotation:
- Semantic annotation – This involves the identification of various concepts such as names or objects within text files to create references upon which an algorithm will learn from.
- Video/image annotation – Here, image recognition is used to help identify different content of interest, in a still or moving context.
- Text/content categorization – This assigning of attributes helps classify written content according to predefined categories
- Entity annotation – In order to make information machine-readable, this process labels unstructured sentences.
How We Apply Data Annotation to Machine Learning
At TranscribeMe, we make the most of our powerful human transcription and human-assisted speech data to train speech recognition engines. This has resulted in the creation of EVA by Voicea – a smart AI virtual assistant for the workplace that helps ensure every important detail and action item is taken note of. With expertly-trained speech recognition systems, voice-based assistance programs like EVA can solve problems faster and take your business productivity to the next level.
We are specialists in transforming large volumes of speech data into client-specific corpora which are consequently used to train AI systems and Automatic Speech Recognition (ASR) platforms. At present, we offer these services in all English accents, Spanish (European & Latin American), Portuguese, Mandarin, Cantonese, Japanese, French and Italian.
We pride ourselves on our ability to deliver highly accurate, human-verified transcription services. The output of these services are further applied to high quality speech recognition training that has a wide range of use cases. With each file we transcribe, our automated speech recognition models improve further. Our robust platform generates better results each time it learns something new. We offer fully-customized AI model training for your speech recognition systems, which includes:
- Custom annotations
- Complete full verbatim transcription
- A multiple step review process
- Capabilities to include customized meta-tags
- Multiple language supportAnd much more!
Interested to know more about how this training service can best be put to use according to your enterprise needs? Request a demo today!
