ASR is an invaluable solution for automatically converting speech to text, and generative artificial intelligence is increasingly perfecting its operation. In this post, you will discover the benefits and challenges of applying speech-to-text in business use cases.
Speech recognition technology, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability that allows a program to process human speech into a written format.
It can automatically convert spoken words into a text transcript. It typically combines AI-powered speech recognition technology with transcription. A computer program captures audio in the form of sound wave vibrations and uses linguistic algorithms to convert the audio input into digital characters, words, and phrases.
Machine learning, deep learning, and large language models, such as OpenAI’s Generative Pre-Trained Transformer (GPT), have made speech-to-text software more advanced and efficient by allowing it to extract patterns in spoken language from large volumes of audio and text samples.
When the Whisper application was released to the general public, this process was made much easier. It is an open source tool, free and easy to use. It is based on deep learning and has been trained on a large amount of speech data to perform multilingual transcriptions and translations. This allows it to adapt to and understand a wide variety of contexts and linguistic nuances.
Today, GenAI can be integrated with speech-to-text software to create assistants that can help a company’s employees or customers over the phone or by interacting with voice-enabled applications. Generative AI can also convert text back to speech, also known as text-to-speech, with a realistic and natural-sounding voice.
Key benefits of ASR
The main advantages of using ASR or speech to text as a business solution include the following:
- The audio interface is very powerful and efficient. The process is agile, fast and comfortable for the user.
- Different types of noise in the environment can be eliminated (denoising), including industrial activities, industrial plants and factory environments.
- It has mobile applications to send audio from the phone and have it processed in the software used by a company.
- It can be integrated with Agentic Workflows, agents that can be integrated into common systems and workflows.
- It is not necessary to transcribe in real time, which is an advantage because it allows corrections and adjustments to be made after the fact.
In the case of the oil and gas industry, speech-to-text technologies allow technical vocabulary to be added to models and improve prediction and prevention, especially in safety and HSE reporting.
Rather than having to wait for the oil engineer to arrive at the office, sit down at the computer and upload the information, this can be done remotely via audio and automatically transcribed into a text record, which is an absolute comparative advantage especially for engineers and operators working in wells and at great distances. On the other hand, it is possible to consult the pressure of a well in real time with specific text2sql queries.
Some issues and challenges
Among the main challenges of ASR applied to the needs of industries and enterprises, it is important to mention
- Although there are many applications and options for speech-to-text today, the error rate remains high, with a Word Error Rate (WER) of 10%. This is mainly due to the problems of vocabulary recognition and voice intonation, which are still being refined in the systems.
- Speech-to-Text technology is essential for the development of conversational agents that are asked for specific information for decision making. However, for critical operations and control commands, it is not yet advisable to implement it. It is clear that the drilling of an oil well cannot be 100% managed or controlled with this technology.
- Speech to text is mixed with the variety of GenAI solutions, using various assistants and real-time applications that provide additional information (for example, if the user wants to know about bitcoin prices or stock market shares) and are integrated into the workflows.
- In speech-to-text models, fine-tuning can be done if we really have audio to do fine-tuning. Mainly corrections in the transcription of the audios with the way of speaking and the differences in dialects. This happens in companies that have a presence in different countries and cultures, especially global companies. In the case of the Oil & Gas industry in general, the technical vocabulary is unambiguous, the processes are well defined, which makes it easy and minimizes model adjustments.
Undoubtedly, end-to-end deep learning models, such as transformers, are fundamental to large language models. They are trained on large unlabeled datasets of audio and text pairs to learn how to match audio to transcripts.
During this training, the model implicitly learns how words sound and which words are likely to occur together in a sequence. The model can also infer grammar and language structure rules to apply on its own. Deep learning consolidates some of the more tedious steps of traditional speech-to-text techniques and is evolving rapidly depending on the use case.
7Puentes: a team of experts in ASR solutions
At 7Puentes, we have extensive experience in developing speech-to-text solutions, with dozens of projects for leading companies.
If ASR is a technology that you want to take full advantage of to integrate audio-to-text models into your processes and combine them with the potential of GenAI, contact our specialists free of charge.
