Blog: 5 crucial steps in the creation of a new speech recognition language

Speech recognition — it seems like magic, doesn't it? Utter a command, and your device obediently responds. But behind this seemingly effortless interaction lies a highly complex and timely process.

In the realm of technology, creating a new speech recognition language is no small feat. It's not just about making words magically appear on a screen; it's about understanding the nuances of human speech, deciphering accents, and capturing the essence of communication in all its diversity.

Within this blog, we unveil the five crucial steps that transform mere words into a comprehensive report or document. We will take you on a journey through the minds of our experts, navigating the layers of complexity to unveil the secrets behind our speech technology.

First and foremost, when we look to create a new language, this is often driven by a business case or need. Be that an introduction to a new geographical area or following a partner requirement. We look to test need and appetite to extend our offering. We look to clearly define the requirements – which language, any dialect considerations and topic or specialism (i.e. Radiology or Employment Law). Once the need and requirements are clear this leads us on to the build…

1. Gathering Quality Data and Identify Specialist Linguists

A crucial part of any language development is good quality, relevant data. And lots of it! Data needs to have sound and text that corresponds and that is domain-specific. Data is gathered from a variety of sources ranging from lexica, journals, or anonymised reports. This data should cover a wide range of topics and styles to ensure the model's robustness. The data is analysed to ensure alignment between text and audio.

The second part of this first step is to source highly linguistically skilled specialists. This would typically be language academics that understand the intricacies and nuances of the language.

2. Create a standard and ‘normalise text’ (using grammar)

This next stage looks to analyse the text data available and prepare for normalisation. This extensive stage involves the creation and definition of the language including defining important and regular words, spellings and symbols and regular characters used within the language including e.g. numbers, date formatting, units etc.

From this normalisation, a corpus list of words is created. Thorough testing is completed to quality check.

With this lexicon and transcription data, the transcription model is then trained. This is an intensive cycle of testing, improving, testing, improving until the model is robust.

3. Test recognition and improve package

Once the model is approved by Q & A for BETA, we begin the testing of the package. This testing is conducted in a contained environment working closely with a partner. Similar to the build of the model – this part of the build includes a cycle of testing and improvement.

4. Fine-Tuning and Iteration

The model is then fine-tune based on feedback and performance evaluation. This may involve collecting more data to further broaden the model.

This process is iterated until we achieve a high performance.

5. Deployment and Evaluation

Once we are confident with the language model, we will look to deploy to our partners for end-user implementation.

We continuously look to monitor and evaluate the language performance, collecting user feedback and making improvements as necessary.

Building a speech recognition language is a highly complex and intensive process. Our team of experts have been involved in speech recognition since its inception.

If you’d like to learn more about our speech recognition SDK or to find out more about becoming a partner, get in touch with our highly knowledgeable team: Recognosco | Contact Us, get in touch with our knowledgeable team