Unveiling the power of speech-to-text algorithms

By: Verbit Editorial

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According to research, one in four consumers regularly use the voice search functions on their mobile devices. Voice recognition and speech-to-text tools are becoming increasingly popular. In fact, experts believe that by the end of 2024, the number of virtual assistants in use around the globe will outnumber the human population. But how do these tools actually work?

Speech-to-text technology has nearly limitless uses, and the artificial intelligence that powers these solutions is constantly learning and evolving to become more accurate and effective. Let’s take a closer look at speech-to-text algorithms and discuss some of the best uses for this remarkable technology.  

Speech-to-Text Algorithms: The Basics

Speech-to-text technology powers many popular tools and applications that consumers use daily. Virtual assistants like Siri and Alexa use this technology to help them respond to verbal commands. Voice typing tools also use these kinds of algorithms to help them convert audio information to written text.

Essentially, speech-to-text algorithms take bite-sized chunks of audio and string them together into a recognizable sequence of corresponding words and characters. These tools rely on automatic speech recognition technology or ASR. ASR is an incredibly useful and versatile technology that makes it fast and easy to convert audio information to text.

In addition to powering straightforward voice typing and voice command tools, speech-to-text algorithms can also produce captions and transcripts for audio and video content. Captioning and transcription can help make media content and communications more accessible to individuals with accessibilities and offer a more streamlined method for producing and maintaining written records of multimedia assets.

Machine Learning and Speech Recognition Algorithms

Speech-to-text tools like virtual assistants and voice command technology use advanced speech recognition algorithms to interpret audio input and respond appropriately. Speech recognition algorithms help computers understand human speech and either convert the speech to human text or respond to a verbal command.

Machine learning techniques deepen speech recognition algorithms’ understanding of human speech and enhance their response capabilities. Below are two common examples of machine learning algorithms that may play a role in speech recognition processes:

  • Connectionist Temporal Classification (CTC) Algorithm: CTC is used to train speech recognition systems to convert audio input to text, even if the length of the audio recording does not align perfectly with the length of a written transcript.
  • Hidden Markov Models (HMMs): Hidden Markov Models in speech-to-text are statistical models that help determine the most likely sequence of words and characters based on sounds from an audio sample.

These machine learning algorithms undergo meticulous training to help computers behave more accurately and intuitively during the speech recognition process. The many nuances of human speech make speech-to-text conversions difficult for computer programs with a very narrow set of parameters. However, machine learning can help computers learn from their mistakes and improve their performance.

Deep Learning in Speech-to-Text Algorithms

In addition to using standard machine learning in speech recognition, many developers employ deep learning techniques for the purpose of improving speech recognition algorithms. Using deep learning for speech-to-text programs helps users convert audio to written text more accurately and efficiently.

Neural networks are popular among developers looking to employ deep learning for speech-to-text algorithms. Also, neural networks are computational models that mimic the structure of a human brain and help computers make intuitive connections. Using neural networks in voice recognition software allows computers to better understand human speech, even with confounding variables like poor audio quality or unusual speech patterns.

Neural networks come into play during the speech recognition process in a few different ways:

  • Feature Extraction: Neural networks can break down and isolate the key features of an audio sample (i.e., pitch, frequency and timing).
  • Acoustic Modeling: Neural networks can model the relationship between acoustic features and linguistic elements like letters and words.
  • Language Modeling: Neural networks can create language models in speech processing. These language models help computers correctly string together words into phrases and sentences.
  • End-to-End Speech Recognition: A single neural network can be responsible for everything from audio data handling in speech recognition to actual speech-to-text conversions, so speech recognition applications can simplify their system architecture.
  • Speaker Recognition: Neural networks can also help computers recognize specific speakers based on certain voice and speech characteristics.

Challenges and Improvements

Even with machine learning and artificial intelligence advances, speech-to-text algorithms still have limitations. Computer-powered transcription services and voice search technology are subject to variable accuracy rates due to the overall complexity of human communication. Several variables can impede a speech-to-text algorithm’s ability to represent audio as written text accurately. These might include the presence of:

  • Background noise
  • Low-quality audio
  • Cross-talk
  • Multiple speakers
  • Diverse accents/dialects

Speech-to-text algorithms learn from their past experiences, which means that an algorithm might struggle to convert speech to text accurately if encountering a particular variable or condition for the first time. The accuracy challenges in speech-to-text technology make it difficult for professionals to rely solely upon AI-powered speech recognition tools for projects that make accessibility the end goal. For this reason, many business leaders and content creators have opted to invest in alternative captioning and transcription solutions that don’t rely solely on artificial intelligence technology.

Are There More Accurate Speech-to-Text Applications?

Professionals can more consistently produce accurate speech-to-text conversions through hybrid solutions like the ones that Verbit offers. Verbit uses a dual approach to transcription that combines advanced AI technology and machine learning techniques with the nuanced understanding and expertise of professional human professionals.

When a user uploads an audio recording to Verbit’s platform, proprietary artificial intelligence software will create a draft. From there, it heads to a professional transcriber for review and editing before users can download or convert the results into a caption file.

Verbit’s speech-to-text software is constantly learning from the input of human professionals, and the additional rounds of human quality control help to ensure all final captions and transcripts are accurate enough to support accessibility requirements. Verbit’s platform also offers live captioning and transcription options so users can support their real-time communications with accurate speech-to-text solutions.

Verbit: A Leader in Speech-to-Text Technology

Speech-to-text algorithms are highly advanced and versatile. This type of AI technology has the power to revolutionize the ways we communicate and preserve information.

As future trends in speech-to-text technology evolve, however, it is important to ensure you are investing in the most accurate speech-to-text tools available. Solutions like Verbit offer enhanced accuracy rates without compromising on speed and cost efficiency so that consumers can maximize the value of their investment in speech-to-text technology. If you’re interested in learning more about Verbit’s unique approach to transcription or if you’d like to learn more about boosting efficiency with speech-to-text technology, reach out today to speak to a member of our team.