Insight

In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add a speech-to-text capability to their applications. Since then, we added support for more languages, enabling customers globally to transcribe audio recordings in 31 languages, including 6 in real-time.

A popular use case for Amazon Transcribe is transcribing customer calls. This allows companies to analyze the transcribed text using natural language processing techniques to detect sentiment or to identify the most common call causes. If you operate in a country with multiple official languages or across multiple regions, your audio files can contain different languages. Thus, files have to be tagged manually with the appropriate language before transcription can take place. This typically involves setting up teams of multi-lingual speakers, which creates additional costs and delays in processing audio files.

The media and entertainment industry often uses Amazon Transcribe to convert media content into accessible and searchable text files. Use cases include generating subtitles or transcripts, moderating content, and more. Amazon Transcribe is also used by operations team for quality control, for example checking that audio and video are in sync thanks to the timestamps present in the extracted text. However, other problems couldn’t be easily solved, such as verifying that the main spoken language in your videos is correctly labeled to avoid streaming video in the wrong language.

Today, I’m extremely happy to announce that Amazon Transcribe can now automatically identify the dominant language in an audio recording. This feature will help customers build more efficient transcription workflows by getting rid of manual tagging. In addition to the examples mentioned above, you can now also easily use Amazon Transcribe to automatically recognize and transcribe voicemails, meetings, and any form of recorded communication.

Introducing Automatic Language Identification
With a minimum of 30 seconds of audio, Amazon Transcribe can efficiently generate transcripts in the spoken language without wasting time and resources on manual tagging. Automatic identification of the dominant language is available in batch transcription mode for all 31 languages. Thanks to sampling techniques, language identification happens much faster than the transcription itself, in the matter of seconds.

If you’re already using Amazon Transcribe for speech recognition, you just need to enable the feature in the StartTranscriptionJob API. Before your transcription job is complete, the response of the GetTranscriptionJob API will tell the dominant language of the audio recording, and its confidence score between 0 and 1. The transcript lists the top five languages and their respective confidence scores.

Of course, if you want to use Amazon Transcribe exclusively for automatic language identification, you can simply process the API response and ignore the transcript. In this case, you should stick to short 30-45 second audio recordings to minimize costs.

You can also restrict languages that Amazon Transcribe tries to identify, by passing a list of languages to the StartTranscriptionJob API. For example, if your company call center only receives calls in English, Spanish and French, then restricting identifiable languages to this list will increase language identification accuracy.

Now, I’d like to show you how easy it us to use this new feature!

Detecting the Dominant Language With Amazon Transcribe
First, let’s try a high quality sample. I’ll use the audio track from one of my breakout sessions at AWS Summit Paris 2019. I can easily download it using the youtube-dl tool.

$ youtube-dl -f bestaudio https://www.youtube.com/watch?v=AFN5jaTurfA
$ mv AWS & EarthCube _ Deep learning démarrer avec MXNet et Tensorflow en 10 minutes-AFN5jaTurfA.m4a video.m4a

Using ffmpeg, I shorten the audio clip to 1 minute.  

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