The speech-to-text technology has made our lives 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. Amazon launched Alexa in 2014 and more than 100m of its Echo and Dot gadgets are available in homes around the world today. Alexa is considered to be the most Intelligent Of All DPAs. We decided to provide you with a quick speech to text comparison of these 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 is the winner.

Google transcribe provides services for 125 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 speech to text 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 speech to text is available in numeric stated as the largest of two hours, the rate of Google speech to text services depends more on the language rather than meaning and audio size. Yet, 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, Amazon Web Services(AWS) has been the most robust and adopted cloud platform in the world. This is visible in the accuracy shown by Amazon Transcribe. It is easier to use and provides texts that need minimal edits. The software can detect different speakers (up to 10) more than the other. It also provides a confidence score that indicates how confident the transcription is with the platform.
While Google service doesn’t provide accurate results, it gives you other features to make up for it. It can identify the language spoken in the multimedia content without any extra alterations. It is also good at cleaning up the voice note by removing background noises, perfecting punctuation, and formatting.

Audio Format

In the case of Google, the audio recording formats can be FLAC, AMR, PCMU, or WAV. Also, SDKs are available for C#, Go, Java, Node.js, PHP, Python, and Ruby. Amazon’s speech to text API supports more common audio types that make it convenient to use, including FLAC, MP3, MP4, or WAV.

Audio Size

While both Amazon Transcribe vs Google Speech platforms provide an audio file size limit of 120 minutes per API call, there is an excellent extra benefit in Google Speech. Google Speech recognition 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.

Whereas, Speech to text 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.

Other Features

As we have compared the platforms about their characteristics, let’s have a look at some more 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 lets users type emoticons. The user has to name the emoticon they want to type in by saying “Add ‘smiling emoji’ or ‘winky emoji face’ for example. But the feature is available only in the English language.

Use Cases

Customer Contact Centers

The customer service experience can be better for you and your customers by using Google Speech or Amazon Transcribe. Google Speech gives an empowering customer service experience by letting you add interactive voice response (IVR) as well as agent conversations to your customer service system. It is possible through the Contact Center AI where you can gain insights into your customers from the calls they make. You can also enhance phone call models by performing analytics on the available conversation data, powered by Google Cloud’s, Contact Center AI which ensures a powerful solution.
Whereas, Amazon Transcribe helps to gain actionable insights by transcribing live customer calls with the help of Amazon Comprehend or other AWS services if your customer contact center is able to do it.

Voice Bots & Sentiment Analysis

Amazon Transcribe can help you extracting the call intent, along with the sentiment from conversations. It can unlock the value held within unstructured voice type call data. These insights can help create a better customer experience by assisting agents in and by providing supervisors with quality management alerts in real-time.
While Google Speech-to-text is in use for the most advanced deep learning neural network, giving state-of-the-art accuracy to automatic speech recognition (ASR). It also helps in sentiment analysis for speech and in unlocking voice commands when combined with Google Natural Language.


Despite Google Speech supported by deep learning and Google Cloud’s Contact Center AI, Amazon has better use cases when it comes to digitizing some complicated activities like documenting clinical conversations and scribe and log court reports
Amazon Transcribe Medical is there to log physician-patient conversations in a digitized text format for the entry into Electronic Health Record (EHR) system or analysis. Since it is a HIPPA compliant service and understands medical terminology, physicians and medical practitioners can focus more on patient care than documentation. They have revolutionized the health-tech Industry.
Likewise, in a courtroom, Amazon Transcribe can act as a court reporter and can capture and digitize court hearings, trials, sworn statements, depositions, and other legal proceedings by scribing it. Seminars, work meetings, and educational classes can be logged and digitized too.

Risk Management

Amazon Transcribe can help in compliance monitoring and risk management by keeping live transcribe for audio files as well as video files into searchable archives. You can index and search across the transcribe logs by using Amazon Elasticsearch. In the same fashion, Amazon Transcribe helps content and media producers and distributors by generating time-stamped subtitles that can be showcased along with the video content. Additionally, it could be useful to localize videos, if used along with the Amazon Translate.


More or less, the two systems offer a similar range of advantages and disadvantages in the field of speech-to-text or known as transcription technology. But both the systems are still not shown any progress towards the businesses supported by podcast or the like who have to deal with the technology. Editing of the text segments is a need for them to become usable. There is a long way to go before these services can provide consistent performance that matches humans. Yet, Google and Amazon both shown major progress when it comes to incorporating Machine Learning algorithms. This made these services more beneficial and futuristic.

Also, now when you look at the use cases, it becomes clear that both the services are making progress towards getting equipped with deep learning and AI, and are facilitating many professions now. Most likely, stenographers, note-takers, and meeting assistant jobs will soon go obsolete and these jobs which are in the hands of humans earlier will be in the hands of Speech-to-text services. Text-to-speech API is redefining the future for more accurate and comprehensive analysis and solutions.