本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
通話後分析輸出
通話後分析記錄會以區段的 turn-by-turn 格式顯示。它們包括通話分類、通話特徵 (響度分數、中斷、未通話時間、通話速度)、通話摘要 (問題、結果和行動項目)、修訂和情緒。此外，在文字記錄末尾處會提供對話特性的摘要。
若要提高準確性並進一步根據您的使用案例自訂文字記錄，例如包括產業專用術語，請在您的通話分析要求中新增自訂詞彙或自訂語言模型。若要遮蔽、移除或標記轉錄結果中不想要的文字 (例如褻瀆性文字)，請新增字彙篩選功能。如果您不確定要傳遞至媒體檔案的語言代碼為何，可以啟用批次語言識別，以自動識別媒體檔案中的語言。
以下章節示範見解層級上的 JSON 輸出範例。如需編譯的輸出，請參閱 編譯的通話後分析輸出。
通話分類
以下是轉錄輸出中類別配對的外觀。此範例顯示 40040 毫秒時間戳記到 42460 毫秒時間戳記的音訊與「positive-resolution」類別相符。在這種情況下，自訂「positive-resolution」類別要求在對話的最後幾秒內的正面情緒。
"Categories": { "MatchedDetails": { "positive-resolution": { "PointsOfInterest": [ { "BeginOffsetMillis":40040, "EndOffsetMillis":42460} ] } }, "MatchedCategories": [ "positive-resolution" ] },
通話特徵
以下是轉錄輸出中通話特徵的外觀。請注意，每個對話回合都會提供響度分數，而所有其他特性則會在文字記錄的末尾處提供。
"LoudnessScores": [87.54,88.74,90.16,86.36,85.56,85.52,81.79,87.74,89.82],..."ConversationCharacteristics": { "NonTalkTime": { "Instances": [], "TotalTimeMillis":0}, "Interruptions": { "TotalCount":2, "TotalTimeMillis":10700, "InterruptionsByInterrupter": { "AGENT": [ { "BeginOffsetMillis":26040, "DurationMillis":5510, "EndOffsetMillis":31550} ], "CUSTOMER": [ { "BeginOffsetMillis":770, "DurationMillis":5190, "EndOffsetMillis":5960} ] } }, "TotalConversationDurationMillis":42460,..."TalkSpeed": { "DetailsByParticipant": { "AGENT": { "AverageWordsPerMinute":150}, "CUSTOMER": { "AverageWordsPerMinute":167} } }, "TalkTime": { "DetailsByParticipant": { "AGENT": { "TotalTimeMillis":32750}, "CUSTOMER": { "TotalTimeMillis":18010} }, "TotalTimeMillis":50760} },
問題、行動項目和後續步驟
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在以下範例中，問題被識別為從字元 7 開始，並於字元 51 結束，這是指文字的以下部分：「我想取消我的食譜訂閱」。
"Content": "Well, I would like to cancel my recipe subscription.", "IssuesDetected": [ { "CharacterOffsets": { "Begin":7, "End":51} } ], -
在以下範例中，結果被識別為從字元 12 開始，並於字元 78 結束，這是指文字的以下部分：「我對您的帳戶進行所有變更，現在已套用此折扣」。
"Content": "Wonderful. I made all changes to your account and now this discount is applied, please check.", "OutcomesDetected": [ { "CharacterOffsets": { "Begin":12, "End":78} } ], -
在以下範例中，行動項目被標識為從字元 0 開始，並於字元 103 結束，這是指文字的以下部分：「我今天將傳送一封包含所有詳細資訊的電子郵件給您，然後下週我會回電以繼續追蹤」。
"Content": "I will send an email with all the details to you today, and I will call you back next week to follow up. Have a wonderful evening.", "ActionItemsDetected": [ { "CharacterOffsets": { "Begin":0, "End":103} } ],
生成式通話摘要
以下是轉錄輸出中生成式通話摘要的外觀：
"ContactSummary": { "AutoGenerated": { "OverallSummary": { "Content": "A customer wanted to check to see if we had a bag allowance. We told them that we didn't have it, but we could add the bag from Canada to Calgary and then do the one coming back as well." } } }
在下列情況下，分析工作將在不產生摘要的情況下完成：
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對話內容不足：對話必須至少包含來自代理和客戶的一個回合。當交談內容不足時，服務會傳回錯誤代碼不足。
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安全護欄：對話必須滿足到位的安全護欄，以確保生成適當的摘要。如果不符合這些護欄，服務會傳回錯誤代碼 FAILED_SAFETY_ 指導方針。
錯誤代碼可以在輸出的Skipped部分AnalyticsJobDetails中找到。您也可以在 GetCallAnalyticsJobAPI 回應CallAnalyticsJobDetails中找到錯誤原因。
錯誤輸出範例
{ "JobStatus": "COMPLETED", "AnalyticsJobDetails": { "Skipped": [ { "Feature": "GENERATIVE_SUMMARIZATION", "ReasonCode": "INSUFFICIENT_CONVERSATION_CONTENT", "Message": "The conversation needs to have at least one turn from both the participants to generate summary" } ] }, "LanguageCode": "en-US", "AccountId": "***************", "JobName": "Test2-copy",...}
情緒分析
以下是轉錄輸出中情緒分析的外觀。
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定性 turn-by-turn 情緒值：
"Content": "That's very sad to hear. Can I offer you a 50% discount to have you stay with us?",..."BeginOffsetMillis":12180, "EndOffsetMillis":16960, "Sentiment": "NEGATIVE", "ParticipantRole": "AGENT"..."Content": "That is a very generous offer. And I accept.",..."BeginOffsetMillis":17140, "EndOffsetMillis":19860, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" -
整個通話的量化情緒值：
"Sentiment": { "OverallSentiment": { "AGENT":2.5, "CUSTOMER":2.1}, -
每個參與者和每四分之一的通話的量化情緒值：
"SentimentByPeriod": { "QUARTER": { "AGENT": [ { "Score":0.0, "BeginOffsetMillis":0, "EndOffsetMillis":9862}, { "Score":-5.0, "BeginOffsetMillis":9862, "EndOffsetMillis":19725}, { "Score":5.0, "BeginOffsetMillis":19725, "EndOffsetMillis":29587}, { "Score":5.0, "BeginOffsetMillis":29587, "EndOffsetMillis":39450} ], "CUSTOMER": [ { "Score":-2.5, "BeginOffsetMillis":0, "EndOffsetMillis":10615}, { "Score":5.0, "BeginOffsetMillis":10615, "EndOffsetMillis":21230}, { "Score":2.5, "BeginOffsetMillis":21230, "EndOffsetMillis":31845}, { "Score":5.0, "BeginOffsetMillis":31845, "EndOffsetMillis":42460} ] } }
PII 修訂
以下是轉錄輸出中 PII 修訂的外觀。
"Content": "[PII], my name is [PII], how can I help?", "Redaction": [{ "Confidence": "0.9998", "Type": "NAME", "Category": "PII" }]
如需詳細資訊，請參閱修訂批次作業中的 PII。
語言識別
若啟用此功能，則轉錄輸出中語言識別的外觀如下。
"LanguageIdentification": [{ "Code": "en-US", "Score": "0.8299" }, { "Code": "en-NZ", "Score": "0.0728" }, { "Code": "zh-TW", "Score": "0.0695" }, { "Code": "th-TH", "Score": "0.0156" }, { "Code": "en-ZA", "Score": "0.0121" }]
在上面的輸出範例中，語言識別將填入語言代碼和可信度分數。分數最高的結果將選為轉錄的語言代碼。如需模式詳細資訊，請參閱識別媒體中的優勢語言。
編譯的通話後分析輸出
為了簡潔起見，以下轉錄輸出的某些內容將以省略符號取代。
此範例包含選用功能-生成呼叫摘要。
{ "JobStatus": "COMPLETED", "LanguageCode": "en-US", "Transcript": [ { "LoudnessScores": [ 78.63, 78.37, 77.98, 74.18 ], "Content": "[PII], my name is [PII], how can I help?",..."Content": "Well, I would like to cancel my recipe subscription.", "IssuesDetected": [ { "CharacterOffsets": { "Begin": 7, "End": 51 } } ],..."Content": "That's very sad to hear. Can I offer you a 50% discount to have you stay with us?", "Items": [...], "Id": "649afe93-1e59-4ae9-a3ba-a0a613868f5d", "BeginOffsetMillis": 12180, "EndOffsetMillis": 16960, "Sentiment": "NEGATIVE", "ParticipantRole": "AGENT" }, { "LoudnessScores": [ 80.22, 79.48, 82.81 ], "Content": "That is a very generous offer. And I accept.", "Items": [...], "Id": "f9266cba-34df-4ca8-9cea-4f62a52a7981", "BeginOffsetMillis": 17140, "EndOffsetMillis": 19860, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" }, {..."Content": "Wonderful. I made all changes to your account and now this discount is applied, please check.", "OutcomesDetected": [ { "CharacterOffsets": { "Begin": 12, "End": 78 } } ],..."Content": "I will send an email with all the details to you today, and I will call you back next week to follow up. Have a wonderful evening.", "Items": [...], "Id": "78cd0923-cafd-44a5-a66e-09515796572f", "BeginOffsetMillis": 31800, "EndOffsetMillis": 39450, "Sentiment": "POSITIVE", "ParticipantRole": "AGENT" }, { "LoudnessScores": [ 78.54, 68.76, 67.76 ], "Content": "Thank you very much, sir. Goodbye.", "Items": [...], "Id": "5c5e6be0-8349-4767-8447-986f995af7c3", "BeginOffsetMillis": 40040, "EndOffsetMillis": 42460, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" } ],..."Categories": { "MatchedDetails": { "positive-resolution": { "PointsOfInterest": [ { "BeginOffsetMillis": 40040, "EndOffsetMillis": 42460 } ] } }, "MatchedCategories": [ "positive-resolution" ] },..."ConversationCharacteristics": { "NonTalkTime": { "Instances": [], "TotalTimeMillis": 0 }, "Interruptions": { "TotalCount": 2, "TotalTimeMillis": 10700, "InterruptionsByInterrupter": { "AGENT": [ { "BeginOffsetMillis": 26040, "DurationMillis": 5510, "EndOffsetMillis": 31550 } ], "CUSTOMER": [ { "BeginOffsetMillis": 770, "DurationMillis": 5190, "EndOffsetMillis": 5960 } ] } }, "TotalConversationDurationMillis": 42460, "Sentiment": { "OverallSentiment": { "AGENT": 2.5, "CUSTOMER": 2.1 }, "SentimentByPeriod": { "QUARTER": { "AGENT": [ { "Score": 0.0, "BeginOffsetMillis": 0, "EndOffsetMillis": 9862 }, { "Score": -5.0, "BeginOffsetMillis": 9862, "EndOffsetMillis": 19725 }, { "Score": 5.0, "BeginOffsetMillis": 19725, "EndOffsetMillis": 29587 }, { "Score": 5.0, "BeginOffsetMillis": 29587, "EndOffsetMillis": 39450 } ], "CUSTOMER": [ { "Score": -2.5, "BeginOffsetMillis": 0, "EndOffsetMillis": 10615 }, { "Score": 5.0, "BeginOffsetMillis": 10615, "EndOffsetMillis": 21230 }, { "Score": 2.5, "BeginOffsetMillis": 21230, "EndOffsetMillis": 31845 }, { "Score": 5.0, "BeginOffsetMillis": 31845, "EndOffsetMillis": 42460 } ] } } }, "TalkSpeed": { "DetailsByParticipant": { "AGENT": { "AverageWordsPerMinute": 150 }, "CUSTOMER": { "AverageWordsPerMinute": 167 } } }, "TalkTime": { "DetailsByParticipant": { "AGENT": { "TotalTimeMillis": 32750 }, "CUSTOMER": { "TotalTimeMillis": 18010 } }, "TotalTimeMillis": 50760 }, "ContactSummary": { // Optional feature - Generative call summarization "AutoGenerated": { "OverallSummary": { "Content": "The customer initially wanted to cancel but the agent convinced them to stay by offering a 50% discount, which the customer accepted after reconsidering cancelling given the significant savings. The agent ensured the discount was applied and said they would follow up to ensure the customer remained happy with the revised subscription." } } } }, "AnalyticsJobDetails": { "Skipped": [] },...}
