Deep Learning to rescue Call Centers in the light of COVID-19
The COVID-19 pandemic has affected and disrupted the normal functioning of our lives, government, institutes and organizations, and industries. One of the unfortunate victims was the call center sector. The whole industry changed overnight. Virtual call centers saw massive growth in this period sustained by cloud, gig economy, and crowdsourcing funds. While the necessity to work from home accelerated this predated trend, we also saw increased dependency on AI, Big data, and cloud, along with the popularity of chatbots and voice-bots.
When the pandemic struck, governments across borders were a little late to impose lockdown restrictions. But, after that happened and the time prior, the travel, airlines, retail, logistics industries suddenly found themselves answering the spike in calls and emails. Even inbound calls kept rising as more residents began seeking reliable guidance and medical information for COVID-19. Besides this tremendous pressure on the understaffed firms, IBM too saw a 40% increase in traffic to Watson Assistant from February to April of this year.
While call centers have long been a focal point of workplace automation, the pandemic has accelerated the process. As the companies are starting to look into new opportunities to grow, AI is present to save the day again by deep learning algorithms and models. And fortunately, this can help call center up the game. The conversion of audio to text involves converting audio into phonemes which are then reassembled into predicted words to generate transcripts. This complex process costs time with minimal accuracy at times.
Unlike the above, an end-to-end deep learning approach uses an optimized CNN (Convolutional Neural Networks)/RNN (Recurrent Neural Networks) hybrid model trained on GPUs. It’s optimized to deliver better accuracy under real-world conditions. Accurate ASR (automatic speech recognition) tools can bridge the pandemic gap by providing an accurate call transcript, analyzing the main talking points, and revealing areas where employees can improve to boost customer satisfaction overall. This does not require the building of more data centers either, as a deep learning-based approach allows companies to choose what elements they want to add when building a speech recognition model and train the model to retrain itself. With the ability to optimize its performance, this makes the deep learning system a better cost-effective, accurate, and efficient alternative to the conventional models.
After companies are equipped with the right resources and have access to data, they must take standardized tests to check word error rates in outputs. This implies curation of nearly 100 segments of audio from random files of customer service calls with each section a minute long, and then get those files labeled by a service such as TranscribeMe for about $100. An excellent standardized test will represent real-world data and include complex conditions – with background noise, multiple speakers, diverse accents, different topics. This shall give companies a great idea if the particular model is strong or weak. For this, using real-world audio will prove further beneficial. Although one has to be careful in selecting the training data. It is important to check the training data and figure out if the model can understand it or not by studying it yourself before. This is crucial as deep learning models cannot learn everything it is fed and shall save much money, time, and effort.