The speech-to-text technology has made our lives extremely easy. Now that there are a lot of use cases in our daily life for this technology too, we need it even more. It lets us save time and effort, and provides the required information in a matter of minutes. Tech giants like Google and Amazon are exploring and empowering this field of speech recognition technologies with the help of their Google Speech and Amazon Transcribe products, respectively. We decided to provide you with a quick comparison of their services by analyzing some features and properties, so you have a clear idea of the pros and cons before you go using one.
When it comes to the number of languages supported by the platforms, Google certainly is the winner.
Google Speech provides speech-to-text services for 119 languages. It supports many of the accents of the English languages, including Australia, Canada, Ghana, UK, India, Ireland, Kenya, New Zealand, Nigeria, Philippines, South Africa, Tanzania, and the US. It also supports many other languages, including Bengali, Hindi, Gujarati, Kannada, Malayalam, Marathi, Tamil, Telugu, and Urdu.
Amazon Transcribe provides support for somewhere around a dozen languages. It allows fewer English accents such as British, Canadian, Australian, to name a few. Other languages include Arabic, Chinese, French, German, Portuguese, and Spanish.
While the speed for AWS Transcribe can be numerically stated as a maximum of two hours, the rate of Google speech-to-text services depends more on the language rather than meaning and audio size. However, it is fair to say that both services are approximately the same when it comes to the speed of converting speech to text.
A large number of users and experts say that AWS Transcribe is better than Google Cloud Speech when it comes to providing ready-to-use text pieces.
For the last 12 years, AWS has been the most robust and widely adopted cloud platform in the world. This can be seen in the accuracy shown by Amazon Transcribe. It is easier to use and provides texts that require minimal edits. The software can detect different speakers (up to 10) more easily than the other. It also provides a confidence score that indicates how confident the transcription is with the platform.
While Google Speech doesn’t provide accurate results, it gives you other features to make up for it. It can automatically identify the language spoken in the multimedia content without any additional alterations. It is also extremely good at cleaning up the voice note by removing background noises, perfecting punctuation, and formatting.
In the case of Google Speech, the audio formats can be FLAC, AMR, PCMU, or WAV. Also, SDKs are available for C#, Go, Java, Node.js, PHP, Python, and Ruby. Amazon supports more common audio types that make it convenient to use, including FLAC, MP3, MP4, or WAV.
While both Amazon and Google’s platforms provide a size limit of 120 minutes per API call, there is an excellent additional benefit in Google Speech. Google Speech-to-text has different long speech and short speech systems. The long speech is for transcription, while the short speech is for simple voice interfaces.
Google Speech provides data privacy to the user with a “data logging” system option. Google uses customer data in this system to learn from it and use it to refine its machine learning models for voice recognition. Users have an option to stop data logging if they do not wish to store the data for a particular project.
On the other hand, Amazon stores its data on the Transcribe to develop machine learning models, and only a few selected workers have access to your data. You can request the deletion of voice recording by contacting AWS support.
As we have compared the platforms concerning their characteristics, let’s have a look at some additional features they both provide.
AWS Transcribe lets you create your own “custom vocabulary”. Through building and maintaining a custom vocabulary, you can extend and tailor AWS Transcribe’s speech recognition software. Custom vocabulary gives AWS Transcribe more information on how to process speech in a multimedia script, which can prove to be crucial in certain use cases.
Additionally, Amazon Transcribe automates punctuations using machine learning where needed.
Google Speech lets users type emoticons. The user has to name the emoticon they want to type in by simply saying “Add ‘smiling emoji’ or ‘winky emoji face’ for example. But the feature is available only in the English language.
More or less, the two systems offer a similar range of advantages and disadvantages in the field of speech-to-text technology. But both the systems are still not completely reliable when it comes to podcast businesses or the like who have to deal with the technology regularly. Editing of the text segments is required for them to become usable. There is a long way to go before these services can provide consistent performance that matches humans. Both companies must incorporate Machine Learning algorithms to make their services more beneficial in the future.