{"id":238,"date":"2020-09-17T05:26:14","date_gmt":"2020-09-17T05:26:14","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/09\/17\/automating-the-analysis-of-multi-speaker-audio-files-using-amazon-transcribe-and-amazon-athena\/"},"modified":"2020-09-17T05:26:14","modified_gmt":"2020-09-17T05:26:14","slug":"automating-the-analysis-of-multi-speaker-audio-files-using-amazon-transcribe-and-amazon-athena","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/09\/17\/automating-the-analysis-of-multi-speaker-audio-files-using-amazon-transcribe-and-amazon-athena\/","title":{"rendered":"Automating the analysis of multi-speaker audio files using Amazon Transcribe and Amazon Athena"},"content":{"rendered":"
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In an effort to drive customer service improvements, many companies record the phone conversations between their customers and call center representatives. These call recordings are typically stored as audio files and processed to uncover insights such as customer sentiment, product or service issues, and agent effectiveness. To provide an accurate analysis of these audio files, the transcriptions need to clearly identify who spoke what and when.<\/p>\n

However, given the average customer service agent handles 30\u201350 calls a day, the sheer volume of audio files to analyze quickly becomes a challenge. Companies need a robust system for transcribing audio files in large batches to improve call center quality management. Similarly, legal investigations often need to efficiently analyze case-related audio files in search of potential evidence or insight that can help win legal cases. Also, in the healthcare sector, there is a growing need for this solution to help transcribe and analyze virtual patient-provider interactions.<\/p>\n

Amazon Transcribe<\/a> is an automatic speech recognition (ASR) service that makes it easy to convert audio to text. One key feature of the service is called speaker identification<\/em>, which you can use to label each individual speaker when transcribing multi-speaker audio files. You can specify Amazon Transcribe to identify 2\u201310 speakers in the audio clip. For the best results, define the correct number of speakers for the audio input.<\/p>\n

A contact center, which often records multi-channel audio, can also benefit from using a feature called channel identification<\/em><\/a>. The feature can separate each channel from within a single audio file and simultaneously transcribe each track. Typically, an agent and a caller are recorded on separate channels, which are merged into a single audio file. Contact center applications like Amazon Connect<\/a> record agent and customer conversations on different channels (for example, the agent\u2019s voice is captured in the left channel, and the customer\u2019s in the right for a two-channel stereo recording). Contact centers can submit the single audio file to Amazon Transcribe, which identifies the two channels and produces a coherent merged transcript with channel labels.<\/p>\n

In this post, we walk through a solution that analyzes audio files involving multiple speakers using Amazon Transcribe and Amazon Athena<\/a>, a serverless query service for big data. Combining these two services together, you can easily set up a serverless, pay-per-use solution for processing audio files into readable text and analyze the data using standard query language (SQL).<\/p>\n

Solution overview<\/h2>\n

The following diagram illustrates the solution architecture.<\/p>\n

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The solution contains the following steps:<\/strong><\/p>\n

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  1. You upload the audio file to the Amazon Simple Storage Service<\/a> (Amazon S3) bucket AudioRawBucket.<\/li>\n
  2. The Amazon S3 PUT event triggers the AWS Lambda<\/a> function LambdaFunction1.<\/li>\n
  3. The function invokes an asynchronous Amazon Transcribe API call on the uploaded audio file.<\/li>\n
  4. The function also writes a message into Amazon Simple Queue Service<\/a> (Amazon SQS) with the transcription job information.<\/li>\n
  5. The transcription job runs and writes the output in JSON format to the target S3 bucket, AudioPrcsdBucket.<\/li>\n
  6. An Amazon CloudWatch Events<\/a> rule triggers the function(LambdaFunction2) to run for every 2 minutes interval.<\/li>\n
  7. The function LambdaFunction2 reads the SQS queue for transcription jobs, checks for job completion, converts the JSON file to CSV, and loads an Athena table with the audio text data.<\/li>\n
  8. You can access the processed audio file transcription from the AudioPrcsdBucket.<\/li>\n
  9. You also query the data with Amazon Athena.<\/li>\n<\/ol>\n

    Prerequisites<\/h2>\n

    To get started, you need the following:<\/p>\n