本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。
通话后分析输出
通话后分析记录按细分的 turn-by-turn 格式显示。它们包括通话分类、通话特点（音量分数、中断、非通话时间、通话速度）、通话摘要（问题、结果和操作项目）、编辑和情绪。此外，转录末尾还提供了对话特点摘要。
为了提高准确性并根据您的用例进一步自定义您的转录（例如包括特定行业的术语），请在您的呼叫分析请求中添加自定义词汇表或自定义语言模型。要屏蔽、删除或标记不希望出现在转录结果中的单词（例如脏话），请添加词汇表过滤。如果您不确定要传递给媒体文件的语言代码，您可以启用批量语言识别以自动识别媒体文件中的语言。
以下各节显示了洞察级别的JSON输出示例。有关编译后的输出，请参见已编译的通话后分析输出。
通话分类
以下是转录输出中的类别匹配介绍。此示例显示从 40040 毫秒时间戳到 42460 毫秒时间戳的音频与“正面解决”类别相匹配。在这种情况下，自定义的“正面解决”类别需要在语音最后几秒钟有正面的情绪。
"Categories": { "MatchedDetails": { "positive-resolution": { "PointsOfInterest": [ { "BeginOffsetMillis":40040, "EndOffsetMillis":42460} ] } }, "MatchedCategories": [ "positive-resolution" ] },
通话特点
以下是转录输出中的通话特点。请注意，针对每个对话轮次都提供了音量分数，而所有其它特点则在转录的末尾提供。
"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} },
问题、操作项目和后续步骤
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在以下示例中，问题被确定为从字符 7 开始，到字符 51 结束，这指的是文本的这一部分：“我想取消我的食谱订阅”。
"Content": "Well, I would like to cancel my recipe subscription.", "IssuesDetected": [ { "CharacterOffsets": { "Begin":7, "End":51} } ], -
在以下示例中，结果被确定为从字符 12 开始，到字符 78 结束，这指的是文本的这一部分：“我对您的账户进行了所有更改，现在已应用此折扣”。
"Content": "Wonderful. I made all changes to your account and now this discount is applied, please check.", "OutcomesDetected": [ { "CharacterOffsets": { "Begin":12, "End":78} } ], -
在以下示例中，操作项被确定为从字符 0 开始，到字符 103 结束，这指的是文本的这一部分：“我今天将向您发送一封包含所有详细信息的电子邮件，下周我会给您回电进行跟进”。
"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} } ],
生成式通话摘要
以下是转录输出中的生成式通话摘要：
"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." } } }
在以下情况下，分析作业将在不生成摘要的情况下完成：
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对话内容不足：对话中必须至少包括客服人员和客户的一次回合。当对话内容不足时，服务将返回错误代码 INSUFFICIENT _ CONVERSATION _ CONTENT。
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安全护栏：对话必须符合安全护栏，以确保生成适当的摘要。当不符合这些护栏时，服务将返回错误代码 FAILED _ _ SAFETY。GUIDELINES
错误代码可以在输出的Skipped部分AnalyticsJobDetails中找到。您也可以在GetCallAnalyticsJobAPI回复CallAnalyticsJobDetails中找到错误原因。
错误输出示例
{ "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",...}
情绪分析
以下是转录输出中的情绪分析。
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定性 turn-by-turn 情绪值：
"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" -
整个通话的定量情绪值：
"Sentiment": { "OverallSentiment": { "AGENT":2.5, "CUSTOMER":2.1}, -
每位参与者和每个通话四等份之一的定量情绪值：
"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编辑
以下是你的转录输出中PII编辑的样子。
"Content": "[PII], my name is [PII], how can I help?", "Redaction": [{ "Confidence": "0.9998", "Type": "NAME", "Category": "PII" }]
有关更多信息，请参阅在批处理作业PII中进行密文。
语言识别
以下是转录输出中的语言识别（如果启用了该功能）。
"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" }]
在上面的输出示例中，语言识别使用置信度分数填充语言代码。将选择分数最高的结果以作为转录的语言代码。有关模式详细信息，请参阅识别媒体中的主要语言。
已编译的通话后分析输出
为简洁起见，在以下转录输出中，某些内容被替换为省略号。
此示例包括可选功能-生成式呼叫摘要。
{ "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": [] },...}
