Did you know that voice search is more popular than typing? The statistics reveal that 71% of customers worldwide prefer using their voice to the old-fashioned method of typing.
Sound recognition is still a difficult mountain to climb, even with respect for all the potential of AI. However, it offers solutions to a great deal of issues facing our society.
But can machines actually listen and understand the audio signal that humans produce? This topic has picked our interest, so let’s figure out the magic behind sound and speech recognition systems in AI together!
How Can Machines Recognize the Sounds?
AI recognition is a powerful capacity that artificial intelligence has developed and evolved over time. When we hear about this concept, we immediately think about image recognition or face recognition technology or even OCR (Optical Character Recognition) of handwritten text.
Sound recognition, however, is a much more complex issue. It’s hard for a machine to capture an audio signal and recognize its meaning compared to simpler visual data, such as images or videos. Nevertheless, the intricate technology of audio recognizer is already firmly entrenched in our lives. Can you name at least one sound recognition application? Let us help you!
For example, some of you can use home assistants that listen to your requests and even perform specific tasks (tell you the news, a joke, find the recipe you need, etc.). Also, many businesses use CRM systems to collect crucial customer data to enhance their service. Another good example is an ML model used to transcript speeches or important business meetings.
As you can see, the application of audio and speech recognition technology is manifold. But still, this solution isn’t given due attention, even though it’s the technology that truly makes our lives, work, and education easier. Let’s fix that and talk about AI sound recognition in more detail!
The Process Behind Audio and Speech Recognition in AI
According to market predictions, the size of the worldwide voice recognition industry would increase from 10.7 billion US dollars in 2020 to 27.16 billion US dollars by 2026. 16.8% is the expected CAGR for the period 2021 to 2026.
In fact, a surprising amount of options exists for the practical use of sound recognition. How do they work in practice?
Audio and speech recognition software processes and translates the sounds into text using computer algorithms. Following these key steps, AI sound recognition technology converts the audio data into text that both computers and people can comprehend:
- Analyzing the audio file;
- Cutting it into separate pieces;
- Processing data into a machine-readable format;
- Using an algorithm, find the best appropriate text representation for it.
Due to how context-specific and extremely varied human speech is, it might be harder for voice recognition algorithms to understand it. Therefore, different patterns of sounds, speaking styles, languages, dialects, accents, and phrasings must be used to train sound recognition algorithms that analyze and turn audio data into text.
The next-level feature of such AI recognition algorithms is their ability to distinguish speech or audio sounds from the ever-present background noise. There are two main types of models used by voice recognition systems to satisfy the above-mentioned criteria:
- Acoustic models: Such a model illustrates the connection between speech linguistics and audio signals.
- Language models: To discriminate between words that sound the same in the given context, this model matches the sound with word sequences.
To sum up, although each task assigned to a distinct audio or speech recognition system has a somewhat different methodology, they all share the same principles of work. A sound recognition model begins by recording the sounds it hears. They may be either live, recorded, or take on several formats. This stage provides the groundwork for all the further sound processing.
After that, the sound recognition model then examines the audio data. In other words, the training data are compared to the recorded sounds. At this stage, the training technique and data quality are two determining factors for how quickly and accurately predictions are produced by an ML model.
Finally, the model produces meaningful data. This can be anything from searching for an answer to your query to transcribing speech into editable text to browsing a database of similar sounds using a classification method to pinpoint the source of the sound.
Speech vs. Audio Recognition: Don’t Get Yourself Confused
In this article, we say it’s audio AND speech recognition technology. But doesn’t this sound the same to you? While your answer can be yes, these are two different types of technologies in machine learning.
To put it simply, you use speech recognition to recognize words in spoken language, that is speech. In contrast, voice recognition is useful for cases when you need an application to identify an individual’s voice.
- Hence, the machine’s capacity to recognize words spoken aloud and translate them accurately into text is known as speech recognition, often known as speech-to-text. Research in computer science, linguistics, and computer engineering are all used for this ML task. Speech recognition features are integrated into plenty of contemporary gadgets and text-focused software to facilitate easier or hands-free usage.
- Voice or speaker recognition refers to a machine’s ability to receive, decipher, and act upon spoken orders. The popularity and use of voice recognition have increased, along with AI and intelligent assistants. You probably know some of them, like Amazon’s Alexa, Apple’s Siri, and Microsoft’s Cortana. Users may connect with technology by speaking directly to it, which enables hands-free requests, reminders, and other basic functions.
Applications of Speech and Audio Recognition
- Mobile gadgets
- Education (language instruction)
- Customer experience and support
- Healthcare (notes transcription into medical records in real-time)
- Support for disabled people with hearing loss
- Emotion recognition
- Court proceedings transcription
- Smart voice assistants
- Hands-free communication and option for calling
- Voice biometrics
- Voice picking (speaker-dependent audio recognition))
As with any other advanced technology, AI sound recognition relies on data. Actually, mass volumes of labeled training data. So, to develop such a system, you need to spend a considerable amount of time gathering and analyzing audio data (in addition to the work of developers and data engineers who build an ML model).
Data annotation is a fundamental step in helping machines understand this audio data in a way we want them to hear it and understand the meaning behind each of the sounds. Many companies seek out data labeling partners since this is a labor-intensive activity that takes a lot of time and effort.
This helps them prioritize the creative process of devising a machine learning model. So it’s best to opt for professional data annotation services, like https://labelyourdata.com/.
Audio and speech recognition with machine learning is a fascinating science of teaching machines to listen, understand how we talk, or even comprehend the sounds and music we create. This technology bears promising potential in the field of AI and is already benefiting our society.
Both of these recognition technologies appear to be similar and distinct at the same time. However, they are indeed closely intertwined in their ability to offer multiple cross-functional solutions that make our lives better in the end. And there are many opportunities the technology presents for the future, too. But don’t forget about the value of well-annotated data for the best possible accuracy and credibility of the received outcomes of sound recognition technology.