How Does Speech Recognition Technology Work?

It seems easy now, but numerous failures and dead ends have hit every advance in speech recognition. Between 2013 and 2017,?Google’s word accuracy rate climbed from 80% to 95%, and it was predicted that half of all?Google searches?would be voice. Although voice-activated personal assistants are still in their?infancy, the worldwide industry is worth?$9.5 billion, with?2.36 million vendors. It took decades to develop speech recognition technology, and we’ve barely scratched the surface.

In this blog, I’ll go over the fundamentals of speech recognition technology, how it works, why accuracy is so important, and the challenges & obstacles to overcome for large-scale adoption & success.

Speech Recognition Fundamentals

Speech recognition technology is a type of artificial intelligence that enables machines to recognize spoken words. It can be used for various purposes, including dictation, translation, and automated customer service interactions.

There are three main aspects of speech recognition technology:

• Automatic speech recognition (ASR): ASR is the task of transcribing the audio.
• Natural language processing (NLP): NLP is used to derive meaning from speech data and the transcribed text.
• Text-to-speech (TTS): TTS converts text into human-like speech, allowing virtual assistants like Amazon Alexa, Apple’s Siri, and Google Home to understand and respond to our requests.

An AI-powered speech recognition engine for any language typically consists of:

• Speech Recognition software: The speech recognition software is responsible for converting spoken words into text.
• Voice/Acoustic Model: The voice model is a template that captures the unique characteristics of a person’s voice.
• Language Model: The language model contains information about grammar and vocabulary.

When someone speaks into a microphone, the speaker’s unique voice template is then broken up into discrete segments, visualized in the form of spectrograms. These spectrograms are further divided into timesteps using the short-time Fourier transform. The speech recognition software uses various algorithms to isolate the sound into smaller segments of several tones or frequencies. These acoustic signals are then analyzed and compared against the voice model.

If the acoustic signals match one of the templates stored in the voice model, that word or phrase can be recognized. The language model is used to help the software determine the meaning of the spoken words.

Each spectrogram is analyzed and transcribed based on an NLP (natural language processing) algorithm. This algorithm makes predictions about all words in a language’s vocabulary, and a contextual layer helps correct any potential mistakes.

The most important aspect of speech recognition technology is its accuracy. It is constantly improving as the algorithms become more sophisticated. However, some challenges still need to be addressed, such as poor listening conditions and accents. Additionally, data privacy is becoming increasingly essential companies collect more and more voice data companies collect more and more voice data.

Why do we need an accurate speech recognition engine?

Speech recognition technology in cars is a good way to keep drivers from typing while driving. If you want to call your friend Billy, and say call “Billy” and the car starts blasting the latest “Billie Eilish” song, it may distract the driver from focusing on the road. There are many less dramatic examples such as in an interview situation, it's very important to know who is speaking and correctly attribute questions and answers. A lot of times, some interviewers spend more time talking than listening. Accuracy plays a major role in the adoption of any technology and speech recognition is no different.

Speech Recognition Technology’s Obstacles

1. Language and Accents: Engineers must be able to comprehend an infinite number of variations, including different languages, dialects, and accents. This implies the acquisition of a lot of data. For example, North American accents and dialects are well-suited for voice recognition software, but it has limited application outside North America. Other nuances such as non-native accents, different genders, and ages could further complicate matters.
2. Audio Quality Challenges: Poor recording tools and best practices could lead to low-quality audio, requiring additional speech recognition handling.?Tip: Tools like Voice-Focus from Amazon can help clean up the audio for noise etc.
3. Multiple speakers: When there are numerous speakers scenarios like an interview or a panel discussion, who said what becomes an essential factor and sentence boundaries, right speech attribution becomes more difficult for speech recognition engines.
4. Abbreviations or Use of Slangs: Abbreviations, especially those which sound like a word such as “ASR” or the use of slang like “Slack me,” pose a challenge to accurately transcribing the speech.
5. Homophones?(words that sound similar): ‘there/their/they’re,’ and ‘right/write’ that sound the same but have different meanings

This technology can revolutionize the way we interact with smart devices and can help us better understand the world around us. This is why speech recognition technology is so important. This technology is still in its early stages, but with continued research and development, it will become more and more accurate.

What do you think about the potential of speech recognition technology? Let me know in the comments below!