Call transcription is the transformation of an audio track either voice or video call into written words that are stored as text in a conversational language. The objective of using this automated call transcription is for people to be able to rapidly examine the calls instead of listening to complete call conversation. People can also search specific words and phrases in all calls within a specific date. The main goal of this is to reduce time as well as get more accurate and efficient results as compared to manual call monitoring or transcription.
Why do we need Call Transcription?
Call monitoring is a fundamental task in the telecommunication industry. But listening and analyzing every call is time consuming and tedious work for telecom agents. And most importantly, you can not get essential information like how an experienced agent handled a problematic customer, etc. This type of data will be useful for newly joined agents at the time of training.
Advantages of call monitoring:
In the case of the marketing department, they can use transcription data for lead generation. The marketing department can know what customers want to optimize their products or services.
Translating audio to text uncovers precisely information disclosed (and while) during calls. The primary advantages of call record: they assist you to make informed strategic key business choices that address and resolve issues; they reduce operator and client churn.
AI-based automatic call transcription models or systems are essential in telecommunication industries to better understand customer emotions towards their services and products.
Advantages of AI-based solutions
But we can resolve this use by using AI-Based models with automatic call transcriptions in less time and all accurate vital aspects.
Telecommunication industries are starting to adopt AI technologies in their company tasks to reduce or save money. The cost of developing such models or systems is too less than hiring agents.
The telecommunications industry has a vast amount of conversational data between client and call center agents. So we can collect audio conversation data from hundreds of hours of telephone calls, which are generated at Telecom call centers. After collecting audio data, we have to convert that audio data into text.
All this data contains sensitive information about clients, so companies require more secrecy, meaning there is no access to audio or any textual data to a third party.
The annotation process for this problem statement is a crucial part of building a model. Here the annotation process is manually transcription that means the manual conversion of audio to text.This problem statement is required not enough to convert verbal content in the audio. We have more information than verbal communication. That is non-speech content during the conversion process. What are these non-speech or non-verbal content is hesitation, laughter, etc. annotator also labeling these types of content with corresponding tags and even labeling with emotions or sentiments related tags (positive, negative, hesitate, laughter, etc.)Here annotators will perform labeling in different aspect like
2. For emotions (sad, satisfied, unsatisfied, happy, etc.)
3. For non-speech content (laughter, hesitate)
4. For keywords extraction, they are labeled with different topics (recharge info, inquiries, complaints, requests ).
Annotation of these types of data and vast amounts of data is a challenging task for an annotator. They have to focus more on during annotation because the model will learn more about data in terms of verbal and non-verbal communication during a call only because of the qualitative annotation process. The result of this qualitative annotation will be becoming a more accurate or better performance model.
To remove these errors, we have to apply spell correction, expand contractions, lower case conversion, etc.
In the preprocessing, we have to remove audio, which is not suitable for training models like if some parts of audio have too much noise or non-speech content, we can ignore that type of audio components. For this, we have to segmented audio data based on their transcriptions. If we feel particular audio segment content is unsuitable for preparing models, then we can reject, at that point, a standard.
The complete process of preprocessing is, first, the corpus is fragmenting into sentences. Non-verbal words (hmm, aa, etc.) and special characters (for example, comma, period, and so forth.) are removing all the tokens; lastly (aside from named-substances) were changed over to lowercase. Conversely, the non-verbal occasions are kept in the preparation text for those models that support event recognition.
At the time building or developing a model, we will use a preprocessed dataset. We have to use different models or different NLP techniques to build the best performance model. Here other models that are useful to construct audio transcription models in the development of the model phase.
Sentiment analysis uses particular words and phrases to identify the customer’s sentiments based on their mood in the call conversation. For example, the customer says, “I am satisfied with your service” This sentence is considered positive sentiment; however, the phrase “I need to speak to a manager” would be getting a negative tag. Like this, the customer sentiment’s total score from their transcribed call will determine whether a customer had an overall positive or negative experience.The use of sentiment analysis allows businesses to make decisions quickly and whatever pain points customer experience etc. The outcome is a better understanding of clients’ needs and a more customized experience. Sentiment analysis of customers can make new income and diminish client churn.
Topic modeling is useful for getting a summary description via topics. In the call transcription, topic models assign different topics to complete call conversation based on what is discussed in that. This technique allows call center agents or technical teams to search through transcripts according to a topic. For instance, via scanning the information for negative keywords, you can rapidly recognize calls where clients are disappointed and figure out how you can improve their involvement with what’s to come.
Building language models to generate language like humans is a challenging task due to data sparseness.Language models provide an excellent meaning to transcription. Language models are used to select which sequences of words are suitable for input to generate corresponding output. They are incredibly helpful to separate terms that sound the equivalent, however, composed unexpectedly.Here we have to consider one more thing: during call conversation, few non-verbal words are coming, such as hmm, aa, ee, etc. These sounds can lead to different meanings (breathing, consent, coughing, hesitation, laughter, and other human noise), etc. Non-word expressions – uncertainty, agreement, and so forth – are a regular piece of human communication. We have to build a separate model to recognize these sounds or non-speech words and avoid confusion between familiar words and these words. This technique’s benefit is that it allows us to generate recognition outputs rich in speechlike communicative expressions.
In the deployment phase of models aims to test our trained model is:
Once we have the tested models we can go live with those models. There are multiple ways to serve these trained models. For this, we will serve them through either Tensorflow Serving or Docker. As these types of deployment models help us to easily scale and manage complicated architectures. This way of deployment models can easily be updated with just one line of command. So no more downtime in real-time.