Automatic versus Ad Hoc Call Transcription

Many companies would undoubtedly benefit from transcribing their telephone calls. To understand why, consider the widespread use of email among companies today. In the past, locating correspondence required physically searching through filing cabinets. With digital communications, instant correspondence searches are possible. However, the question arises: why do employees search historic emails but not historic phone calls? The prevailing reason for companies not transcribing calls is the perception that machines are either not accurate enough or the process is too expensive. This perception is influenced by user experiences with digital assistants like Alexa and Siri, or the use of commercial BOTs by financial institutions to avoid human operator costs.

However, it is important to note that the technology and human factors design experienced in the business and retail markets are not representative of the current state of the art. Machine transcription of spoken words can be as good as, if not better than, human transcription. While machines may struggle with understanding speech without context, humans face the same challenge. Moreover, machines typically cost only a fraction of what a human would, and they can produce transcriptions at least as fast as human speech.

Aside from performance and cost considerations, most companies currently transcribe telephone calls only in response to issues or disputes with customers. Since disputes should not be the norm, transcribing all calls speculatively seems unnecessary. Therefore, call transcription is usually done on an ad hoc basis once call recordings have been retrieved. However, given the potentially large volume of calls a company deals with daily, locating a specific call becomes time-consuming. Ad hoc transcription relies on knowing the exact time and date of each call, similar to imagining that retrieving a historic email required entering the precise date and time it was sent or received. The ability to search for keywords in emails enables users to narrow down their search to something feasible, if not unique. To achieve this level of efficiency, every call should be transcribed.

Only five years ago, machine-transcribing every call would have been neither cost-effective nor accurate enough to warrant implementation. However, with word recognition rates now comparable to humans and transcription costs lower than £3 per hour, the landscape has changed. Nonetheless, to fully benefit from machine call transcription, companies must adapt their workflows to align with improvements in speech recognition technology.

Identifying individual phone calls and submitting them to a transcription service in an ad hoc manner is bound to fail. Extracting digitized speech for transcription poses a challenge, and determining how to handle transcribed speech adds complexity. Many companies now use private branch exchanges (PBXs), either on-site or in the Cloud. Merely finding a call within a sequence of transfers is already difficult, but ensuring it is recorded at the highest quality is equally crucial. Once the recording is extracted, it needs to be indexed in a database for easy future retrieval along with relevant metadata such as party names, numbers, dates, and times. In short, ad hoc call transcription falls short in every aspect. Automatic call transcription is the only viable solution. In other words, the process of automatically extracting, transcribing, and storing calls must be implemented, as companies relying on manual ad hoc methods will soon realize their investment is wasted.

While algorithms used in transcription are vital, they become useless without the necessary infrastructure for processing. As the number of employees using a single phone system increases, the process of isolating conversations and delivering specific transcriptions to the appropriate staff becomes as complex as the speech recognition process itself.

Numerous reasons exist to transcribe calls, even when disputes are not anticipated. Simply taking notes on call outcomes is a standard practice in many professions and often consumes as much time as the call itself. The higher the salary of the staff member, the greater the overhead involved. Telephone calls, like emails, shape how a company operates. The information contained within calls is invaluable, providing insights into how third parties are handled and the issues they face. Real-time information on company operations can be obtained without relying on outdated surveys or questionnaires. The exponential growth of artificial intelligence enables even small companies to extract previously inaccessible information.

This is the true essence of automatic call transcription. It empowers companies to understand their operations and make data-driven improvements based on constantly evolving insights.


Automatic call transcription plays a crucial role in modern businesses. By transcribing telephone calls, companies can uncover valuable insights, streamline operations, and leverage active data analysis. Machine transcription has reached a level of accuracy and cost-effectiveness that surpasses previous limitations. However, to fully capitalize on its benefits, companies need to adapt their workflows and implement automated transcription processes. With the rapid advancements in artificial intelligence, businesses of all sizes can harness the power of call transcription to enhance decision-making and drive continuous improvement.