What Speech Recognition and the Human Brain Have in Common

Fun fact : The speech recognition system might not be the number-one speech processing, but it sure learns from the best—the human brain.

We, humans, tend to find patterns in everything.

Try ask your therapist.

That’s also how AI learns from the human brain. Speech recognition, in particular, shares some common principles in understanding and interpreting spoken language with this complex organ, our brain, which is a major part of the central nervous system.

But first, let’s get a bit more technical.

What is Speech Recognition? Speech Recognition VS Voice Recognition

Speech recognition, known as automatic speech recognition (ASR), is a technology that converts human speech or spoken language into written text. Such process requires computer science, linguistics, and computer engineering to translate and transcribe.

Meanwhile, Voice recognition, which is often mistaken for speech recognition, is a technology that can recognize and identify an individual’s voice by incorporating similar fields of study.

6 Functions Speech Recognition and The Human Brain Have in Common

1. Pattern Recognition

Both speech recognition systems and the human brain rely on pattern recognition. Typically, a speech recognition program requires analyzing acoustic patterns and identifying linguistic features like pitch, intensity, and duration of sounds.

The human brain also excels at recognizing patterns in speech, whether from individual characteristics or linguistic structures, allowing us to understand and interpret language.

2. Feature Extraction

Both systems extract relevant features from the input signal. In terms of speech recognition software, this involves extracting acoustic features like pitch, intensity, and duration. Similarly, the human auditory system extracts these features to comprehend spoken language. 

3. Contextual Understanding

Speech recognition resembles how the human brain uses contextual information to enhance understanding. Machines leverage context through language models and context windows, while the human brain integrates linguistic and situational context to derive meaning from speech. 

Sometimes, a speaker might indicate sarcasm and symbolic meaning or incorporate more than one language in a conversation.

4. Learning and Adaptation

Both speech recognition and the human brain can learn and adapt over time. Machine learning algorithms enable speech recognition software to improve accuracy through exposure to more data.

Meanwhile, the human brain takes it to another level by learning and adapting to different accents, languages, and speaking styles.

5. Neural Processing

The human brain and neural networks in machine learning share some conceptual similarities. Neural networks in machine learning are inspired by the structure and functioning of the human brain, with layers of interconnected nodes or neurons processing information.

6. Error Handling

Both systems encounter errors and uncertainties. Speech recognition systems may misinterpret words, and the human brain may mishear or misunderstand. 

In both cases, there is a need to employ context, knowledge, and other cues to resolve ambiguities and arrive at the most likely interpretation.

Same but different, Similar but not identical

While the processes are analogous, it's essential to note that the level of complexity and efficiency in the human brain's speech processing far exceeds the capabilities of the speech recognition system. 

The human brain integrates information from various sensory and cognitive processes, establishing new sets of theories, solving problems, imagining, and recalling the last encounter you had with a particular person.

Incorporating Speech Recognition for Business Enhancement 

As businesses have been looking for solutions to better customer experience in every way possible, it’s safe to say that the demand for speech recognition has increased, too. 

Recent Google Search reveals that 27% of the online global population uses voice search on mobile devices, which contributes up to 1 billion voice searches monthly.

With 93.7% of queries being handled by voice search assistants, it proves how effective speech recognition has been integrated into AI technology.

Take Amity Solutions’ AI Voicebot for example.

Having GPT-based technology and chatbot integrations handle requests and queries can efficiently improve customer experience, allowing human-like conversations from both sides.

With two forms of input and output—voice and text—available in Thai, Amity Voicebot can be an engagement-worthy add-on to your call center system and customer service.

For more information, click here.