The talk around town is that speech recognition software can help businesses to operate more efficiently, safely, and cost-effectively. With all that good news, you may be wondering, what exactly is speech recognition software, and how does it work?
Luckily, you’ve come to the right place because we are going to uncover all the definitions you need to know. We’ll also uncover what to expect of this technology moving forward.
What is Speech Recognition Software?
Speech recognition software refers to a computer program that deciphers and understands human speech to transcribe it into text. Also called automatic speech recognition (ASR) or speech-to-text, the software can be used to:
- Automate processes
- Improve accessibility for those with disabilities
- Enhance productivity
- Capture otherwise lost data
Speech recognition software is a form of artificial intelligence (AI) that is trained on language models. These language models rely on having access to large sets of text data that begin to learn the structure of language to then apply it to spoken word. In this way, speech recognition software can mimic how humans communicate by listening and responding.
Basic Components of Speech Recognition Software
For speech recognition software to work, there are some main technological components that play a role, such as:
Natural Language Processing (NLP)
Natural language processing (NLP) is machine learning technology that enables computers to interpret and comprehend language. NLP is able to not only process data, but it can also analyze sentiments to interpret intent and emotion behind speech. NLP works through the combination of machine learning, deep learning, and computational linguistics.
Machine Learning and Artificial Intelligence
Both machine learning (ML) and artificial intelligence (AI) is critical to speech recognition software’s functioning. Machine learning includes neural networks and deep learning that can use structure, grammar, and the composition of audio and voice in order to process speech. Artificial intelligence enables speech recognition software to understand the context of words, whereas machine learning is applied to be able to understand accents and different pronunciations of words.
When it comes to speech recognition software and the technology that makes it possible, accuracy is always the top concern, especially for business use cases. aiOla offers a first-of-its-kind speech AI technology that can understand business-specific jargon without having to be previously trained. By adapting existing language models, this means that aiOla can automatically understand your business’ customized vocabulary to effectively assist in process completion. Along with understanding these unique keywords, aiOla knows over 100 languages, and can decipher relevant speech in any accent and acoustic environment.
What You Need to Know: Key Terminology
Given the many uses of speech recognition technology, it’s helpful to have a better understanding of its nuances and how it works. Here are some key terms that are useful to know to paint a clearer picture of all that is involved:
- Speech-to-Text (STT): Speech-to-text (ST) is the technology that enables software to recognize and translate spoken words into written text. It listens to audio to then transcribe it into text that is editable on a device. STT is used for voice typing, chatbots, and transcription, for example.
- Text-to-Speech (TTS): As you’ve probably guessed, text-to-speech works in the opposite way as speech-to-text. It is assistive technology that reads written text out loud.
- Wake Word: If you used Apple’s Siri or Amazon’s Alexa, then you’re probably already familiar with the idea of a “wake word.” In these cases, it’s “Hey, Siri” or “Alexa.” A wake word is a phrase or word that activates the speech-enabled device when you wish to speak to it.
- Phoneme: In linguistics, a phoneme is what’s known as the smallest unit of speech, which helps to decipher one word from another. This is necessary for voice and speech recognition software to be able to understand speech as the software breaks down spoken words into phonemes. Then, it represents them digitally as ones or zeroes. Once these phonemes move through a data set, or dictionary, the software can determine what word was being said.
The Various Applications
From personal use to professional use, there are so many applications for speech recognition software.
Whether you’ve experienced them yourself or are considering implementing a speech AI solution in your business, these are some ways they come in handy:
- Virtual Assistants (e.g., Siri, Alexa, Google Assistant)
- Transcription Services
- Dictation Software
- Voice-Controlled Devices
Challenges and Considerations
Technology, such as speech recognition software, is intended to be beneficial and advantageous. But, as with any innovation, there are considerations to keep in mind. For example:
Accuracy and Error Rates
Perhaps one of the biggest challenges and concerns is the tool’s accuracy to understand spoken words. The common metric to assess a speech recognition software’s capabilities is known as the word error rate (WER). This is a summation of the words that were missed or incorrectly understood from the audio. (Did you know that aiOla boasts a highly impressive word error rate of just 5%?).
Noise and Environment
Accuracy is not only impacted by the model, but it is also affected by background noise and the acoustic environment in which you operate. That’s why it’s great to find a solution like aiOla that can work in any environment, even manufacturing floors. Additionally, aiOla is able to discern what vocabulary is relevant for the process and task at hand, separating it from surrounding conversational chatter.
Speaker Variability
Everyone speaks differently, and speaker variability refers to the nuances in speaking rate, affect, and intensity.
Privacy and Security Concerns
Another really valid consideration is privacy and security, especially when these solutions listen to sensitive and even proprietary information.
A Look at Future Trends
While speech recognition software has been around for decades, it is only gaining in its popularity and usage. Innovations and advancements continue to propel the industry forward.
Deep learning and neural networks are becoming more powerful. At the same time, speech recognition tools are integrating with the Internet of Things (IoT) and smart devices to create an intelligent ecosystem to get more done, in less time, with fewer resources.
There’s a lot to look forward to when it comes to the future of speech-enabled devices. To get a sense of specific future expectations, take a look at these resources:
- Future of AI in Automotive Industry
- Future of AI in Manufacturing
- Future of AI in Logistics
Closing Thoughts
While people can type quickly, they can certainly speak even more quickly. Speech recognition software provides many benefits in both the personal and professional realm. When it comes to uses in organizations, voice and speech recognition systems are able to increase efficiency, boost safety, enable collaboration, capture otherwise lost data, and offer ש better way to get work done.
