What Is Word Error Rate in Automatic Speech Recognition and Why Is it Useful?
A huge number of industries are now interested in automating as many tasks as possible. One of the most demanded tasks is automating interaction with support, as well as obtaining important data from conversations with customers, clients, business partners, and so on. Thus, various industries are starting to use automatic speech recognition technology more and more.
The call transcription industry has changed dramatically in recent years. Today, users can ask Alexa for any information, and companies are using software that helps them negotiate or train new hires much more effectively.
Accurate transcripts can be used in many industries. For example, it is a great tool for training sales staff. Exemplary calls with clients or buyers can be transcribed so that newcomers learn much faster. Companies use a variety of sales coaching tools like Revenue Grid and others, which, combined with accurate transcriptions, can lead to more effective sales training.
However, to obtain the most accurate data from customers or employees from telephone conversations, there must be an accurate transcription of the call. One of the most important stumbling blocks in technology is the Word Error Rate. Imperfect systems still cannot transcribe speech with 100% accuracy. To date, Google claims that their rate is only about five percent, while Microsoft’s error is just over 5%.
Such indicators cannot be called ideal. However, to achieve high performance in automatic speech recognition, it is necessary to understand the Word Error Rate, as well as to engage in optimization.
What Does This Rate Mean?
You can determine the accuracy of transcription by calculating a coefficient showing the number of errors in words. This ratio represents the percentage of erroneously transcribed words compared to the original recording.
In practice, if the Word Error Rate is thirty percent or more, this is an unreadable transcription. For example, your speech consists of 100 words, 70 of which are incorrectly transcribed. This transcription cannot be used.
What Factors Influence The Word Error Rate?
Among the most popular speech recognition interfaces, the average Word Error Rate is about twenty-five percent. They are far from ideal, as many factors make it difficult to get an accurate transcription of conversations. Here are some of them:
- Functional lexis
When ordering a transcription of a recording from specialists, you will find that the cost of transcribing recordings with niche terms will be much more expensive. Recognition of jargon is challenging for both people and trained systems. However, for companies in various industries, this factor is extremely important, but most modern systems are far from accurate recognition of specialized words and niche terms.
- Speech with an accent
Another difficulty that prevents obtaining accurate transcriptions is the recording of speech with an accent. Speech recognition systems are trained in most cases for well-delivered speech that can be heard on TV or radio. However, within the same country, people can speak with different accents. This creates some challenges that can affect how marketing departments function, as well as sales departments that want to achieve sales productivity.
- Noise in the background
It is hardly possible that all calls come from sound studios, where the probability of noise in the background is zero. There will always be voices of passing people or colleagues, the noise of cars, or the noise of the wind in the background. This creates certain difficulties for speech recognition systems. However, this factor may not be a problem if your system is trained to suppress background noise.
How Important Are Accurate Transcriptions?
The list of areas where transcriptions can be useful is endless. Any technology now finds application in many industries. As automatic speech recognition technology improves, businesses and companies will be able to achieve better results.
Here are a few reasons why transcription accuracy is important:
- Customer service improvement
Now, in companies around the world, improving customer service is a priority. Companies use all kinds of chatbots, as well as call center specialists who could prompt and help customers solve their problems or satisfy their needs.
When resolving customer problems, it’s incredibly important to act quickly so you can retain the customer. If managers could listen in on the calls of call center specialists in real time, studying the most accurate transcriptions of calls, they could help specialists deal with complex issues that require intervention much faster.
- Rapid training of new employees
The ability to quickly train new employees allows companies to get qualified specialists who are ready to bring excellent results much faster. In sales departments, the ability to study call transcripts is an extremely useful tool.
By studying the recordings of conversations, as well as their transcriptions, beginners can quickly learn how to negotiate. Thus, automatic speech recognition systems, in compatibility with other training tools, can help quickly prepare new employees for important tasks.
- Improvement of teaching materials
Based on call transcripts, company managers can regularly update employee training materials to complete tasks and solve problems faster and more efficiently. This can be used both in the field of work of the call center, sales department and in many others.
By reviewing transcripts of conversations with customers in need of assistance, managers can discover the most common problems. After that, they can train employees to solve these issues much faster.
- Rapidly make changes and predict customer behavior
Based on the analyzed data from transcriptions, business leaders can draw on a lot of essential information. It helps to keep track of the mood of users, buyers, or business partners and is also a great way to get feedback.
You can predict whether your company is facing customer churn or whether your products or services are in urgent need of transformation and improvement. Such information can be regularly collected and strategies for the work of various departments can be thought out.
Conclusion
Among the many useful tools and software, automatic speech recognition technology is still far from ideal. To determine the quality of transcriptions, it is necessary to calculate the Word Error Rate to understand what kind of training the system needs. Accurate transcriptions of conversations can make a positive difference in how many industries work.