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Quickstart: Post-call transcription and analytics

Language service documentation | Language Studio | Speech service documentation | Speech Studio

In this C# quickstart, you perform sentiment analysis and conversation summarization of call center transcriptions. The sample will automatically identify, categorize, and redact sensitive information. The quickstart implements a cross-service scenario that uses features of the Azure Cognitive Speech and Azure Cognitive Language services.

Tip

Try the Language Studio or Speech Studio for a demonstration on how to use the Language and Speech services to analyze call center conversations.

To deploy a call center transcription solution to Azure with a no-code approach, try the Ingestion Client.

The following Azure AI services for Speech features are used in the quickstart:

The Language service offers the following features that are used:

Prerequisites

Important

This quickstart requires access to conversation summarization. To get access, you must submit an online request and have it approved.

The --languageKey and --languageEndpoint values in this quickstart must correspond to a resource that's in one of the regions supported by the conversation summarization API: eastus, northeurope, and uksouth.

Run post-call transcription analysis with C#

Follow these steps to build and run the post-call transcription analysis quickstart code example.

  1. Copy the scenarios/csharp/dotnetcore/call-center/ sample files from GitHub. If you have Git installed, open a command prompt and run the git clone command to download the Speech SDK samples repository.

    git clone https://github.com/Azure-Samples/cognitive-services-speech-sdk.git
    
  2. Open a command prompt and change to the project directory.

    cd <your-local-path>/scenarios/csharp/dotnetcore/call-center/call-center/
    
  3. Build the project with the .NET CLI.

    dotnet build
    
  4. Run the application with your preferred command line arguments. See usage and arguments for the available options.

    Here's an example that transcribes from an example audio file at GitHub:

    dotnet run --languageKey YourResourceKey --languageEndpoint YourResourceEndpoint --speechKey YourResourceKey --speechRegion YourResourceRegion --input "https://github.com/Azure-Samples/cognitive-services-speech-sdk/raw/master/scenarios/call-center/sampledata/Call1_separated_16k_health_insurance.wav" --stereo  --output summary.json
    

    If you already have a transcription for input, here's an example that only requires a Language resource:

    dotnet run --languageKey YourResourceKey --languageEndpoint YourResourceEndpoint --jsonInput "YourTranscriptionFile.json" --stereo  --output summary.json
    

    Replace YourResourceKey with your Azure AI services resource key, replace YourResourceRegion with your Azure AI services resource region (such as eastus), and replace YourResourceEndpoint with your Azure AI services endpoint. Make sure that the paths specified by --input and --output are valid. Otherwise you must change the paths.

    Important

    Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Azure AI services security article for more information.

Check results

The console output shows the full conversation and summary. Here's an example of the overall summary, with redactions for brevity:

Conversation summary:
    issue: Customer wants to sign up for insurance.
    resolution: Customer was advised that customer would be contacted by the insurance company.

If you specify the --output FILE optional argument, a JSON version of the results are written to the file. The file output is a combination of the JSON responses from the batch transcription (Speech), sentiment (Language), and conversation summarization (Language) APIs.

The transcription property contains a JSON object with the results of sentiment analysis merged with batch transcription. Here's an example, with redactions for brevity:

{
    "source": "https://github.com/Azure-Samples/cognitive-services-speech-sdk/raw/master/scenarios/call-center/sampledata/Call1_separated_16k_health_insurance.wav",
// Example results redacted for brevity
        "nBest": [
          {
            "confidence": 0.77464247,
            "lexical": "hello thank you for calling contoso who am i speaking with today",
            "itn": "hello thank you for calling contoso who am i speaking with today",
            "maskedITN": "hello thank you for calling contoso who am i speaking with today",
            "display": "Hello, thank you for calling Contoso. Who am I speaking with today?",
            "sentiment": {
              "positive": 0.78,
              "neutral": 0.21,
              "negative": 0.01
            }
          },
        ]
// Example results redacted for brevity
}   

The conversationAnalyticsResults property contains a JSON object with the results of the conversation PII and conversation summarization analysis. Here's an example, with redactions for brevity:

{
  "conversationAnalyticsResults": {
    "conversationSummaryResults": {
      "conversations": [
        {
          "id": "conversation1",
          "summaries": [
            {
              "aspect": "issue",
              "text": "Customer wants to sign up for insurance"
            },
            {
              "aspect": "resolution",
              "text": "Customer was advised that customer would be contacted by the insurance company"
            }
          ],
          "warnings": []
        }
      ],
      "errors": [],
      "modelVersion": "2022-05-15-preview"
    },
    "conversationPiiResults": {
      "combinedRedactedContent": [
        {
          "channel": "0",
          "display": "Hello, thank you for calling Contoso. Who am I speaking with today? Hi, ****. Uh, are you calling because you need health insurance?", // Example results redacted for brevity
          "itn": "hello thank you for calling contoso who am i speaking with today hi **** uh are you calling because you need health insurance", // Example results redacted for brevity
          "lexical": "hello thank you for calling contoso who am i speaking with today hi **** uh are you calling because you need health insurance" // Example results redacted for brevity
        },
        {
          "channel": "1",
          "display": "Hi, my name is **********. I'm trying to enroll myself with Contoso. Yes. Yeah, I'm calling to sign up for insurance.", // Example results redacted for brevity
          "itn": "hi my name is ********** i'm trying to enroll myself with contoso yes yeah i'm calling to sign up for insurance", // Example results redacted for brevity
          "lexical": "hi my name is ********** i'm trying to enroll myself with contoso yes yeah i'm calling to sign up for insurance" // Example results redacted for brevity
        }
      ],
      "conversations": [
        {
          "id": "conversation1",
          "conversationItems": [
            {
              "id": "0",
              "redactedContent": {
                "itn": "hello thank you for calling contoso who am i speaking with today",
                "lexical": "hello thank you for calling contoso who am i speaking with today",
                "text": "Hello, thank you for calling Contoso. Who am I speaking with today?"
              },
              "entities": [],
              "channel": "0",
              "offset": "PT0.77S"
            },
            {
              "id": "1",
              "redactedContent": {
                "itn": "hi my name is ********** i'm trying to enroll myself with contoso",
                "lexical": "hi my name is ********** i'm trying to enroll myself with contoso",
                "text": "Hi, my name is **********. I'm trying to enroll myself with Contoso."
              },
              "entities": [
                {
                  "text": "Mary Rondo",
                  "category": "Name",
                  "offset": 15,
                  "length": 10,
                  "confidenceScore": 0.97
                }
              ],
              "channel": "1",
              "offset": "PT4.55S"
            },
            {
              "id": "2",
              "redactedContent": {
                "itn": "hi **** uh are you calling because you need health insurance",
                "lexical": "hi **** uh are you calling because you need health insurance",
                "text": "Hi, ****. Uh, are you calling because you need health insurance?"
              },
              "entities": [
                {
                  "text": "Mary",
                  "category": "Name",
                  "offset": 4,
                  "length": 4,
                  "confidenceScore": 0.93
                }
              ],
              "channel": "0",
              "offset": "PT9.55S"
            },
            {
              "id": "3",
              "redactedContent": {
                "itn": "yes yeah i'm calling to sign up for insurance",
                "lexical": "yes yeah i'm calling to sign up for insurance",
                "text": "Yes. Yeah, I'm calling to sign up for insurance."
              },
              "entities": [],
              "channel": "1",
              "offset": "PT13.09S"
            },
// Example results redacted for brevity
          ],
          "warnings": []
        }
      ]
    }
  }
}

Usage and arguments

Usage: call-center -- [...]

Important

You can use a multi-service resource or separate Language and Speech resources. In either case, the --languageKey and --languageEndpoint values must correspond to a resource that's in one of the regions supported by the conversation summarization API: eastus, northeurope, and uksouth.

Connection options include:

  • --speechKey KEY: Your Azure AI services or Speech resource key. Required for audio transcriptions with the --input from URL option.

  • --speechRegion REGION: Your Azure AI services or Speech resource region. Required for audio transcriptions with the --input from URL option. Examples: eastus, northeurope

  • --languageKey KEY: Your Azure AI services or Language resource key. Required.

  • --languageEndpoint ENDPOINT: Your Azure AI services or Language resource endpoint. Required. Example: https://YourResourceName.cognitiveservices.azure.com

Input options include:

  • --input URL: Input audio from URL. You must set either the --input or --jsonInput option.
  • --jsonInput FILE: Input an existing batch transcription JSON result from FILE. With this option, you only need a Language resource to process a transcription that you already have. With this option, you don't need an audio file or a Speech resource. Overrides --input. You must set either the --input or --jsonInput option.
  • --stereo: Indicates that the audio via ```input URL` should be in stereo format. If stereo isn't specified, then mono 16khz 16 bit PCM wav files are assumed. Diarization of mono files is used to separate multiple speakers. Diarization of stereo files isn't supported, since 2-channel stereo files should already have one speaker per channel.
  • --certificate: The PEM certificate file. Required for C++.

Language options include:

  • --language LANGUAGE: The language to use for sentiment analysis and conversation analysis. This value should be a two-letter ISO 639-1 code. The default value is en.
  • --locale LOCALE: The locale to use for batch transcription of audio. The default value is en-US.

Output options include:

  • --help: Show the usage help and stop
  • --output FILE: Output the transcription, sentiment, conversation PII, and conversation summaries in JSON format to a text file. For more information, see output examples.

Clean up resources

You can use the Azure portal or Azure Command Line Interface (CLI) to remove the Azure AI services resource you created.

Next steps