Speech Insights (BETA)

Enable this model configuration to get useful conversational insights that can measure or help measure many of your KPIs.

This API is in BETA and will be provided on request. Please contact [e-mail] to enable this API.

Overview

Perform in-depth analysis of conversational data to visualize trends on topics, sentiments, keywords, and behaviors to achieve better outcomes.

Marsview provides a way to capture the engagement level of speakers in real-time. Additionally, you can track user sentiment and emotions along with engagement data.

Insights

For each conversation/file uploaded it returns the following

Insight	Description
Talk-to-listen Ratio	Speaker’s talk and listen ratio and time
Speech Insights	Insights based on speakers such as- Longest monologue, filler words used, speech clarity, etc.
Call Sentiment Score	Gives an overall assessment of the conversation sentiment based on the sentiments, emotions, and tone used in the conversation
Call Engagement Score	Gives an overall assessment of the conversation engagement based on the talk-time, dead air, and other factors.
Call Score	Scores the call based on different quantitative and qualitative measurements of the conversation. This can be further customized to the business need.
Avg. Speech Speed	Get speech speed by the speaker in terms of WPM (words per minute)
Sentiment vs Time	Capture variations in sentiment over the course of the call by each speaker individually and combined.
Phrase Cloud (by Topics Type)	Captures salient topics found or spoken in the conversation.
Topic Sentiment over Time	Capture variations in sentiment over the course of the call by each speaker individually and combined along with the corresponding topics mentioned.
Speaker Emotions over Time	Capture variations in emotions over the course of the call by each speaker individually and combined.
Dead Air	timestamps of dead air (silence) found during the conversation

modelTypeConfiguration

Key	Value
modelType	data_insights
modelConfig	Model Configuration object for data_insights

modelConfig Parameters

modelConfig	Description	Defaults
dead_air.threshold	The time threshold(in milliseconds) beyond which silence in a meeting should be considered as dead air time.	3000

Example Request

Curl

curl --location --request POST 'https://api.marsview.ai/cb/v1/conversation/{{userId}}/compute' \
--header 'Content-Type: application/json' \
--header "Authorization:{{Insert Auth Token}}" \
--data-raw '{
        "userId":"{{Insert User ID}}",
        "txnId": "{{Insert txn ID}}",
        "enableModels":[
            {
            "modelType":"speech_to_text",
                "modelConfig":{
                    "automatic_punctuation" : true,
                    "custom_vocabulary":["Marsview", "Communication"],
                    "speaker_seperation":{
                        "num_speakers":2
                    },
                    "enableKeywords":true,
                    "enableTopics":true
                    }  
            },
            {
            "modelType":"emotion_analysis"
            },
            {
            "modelType":"sentiment_analysis"
            },
            {
            "modelType":"data_insights",
            "modelConfig": {
                "dead_air": {
                    "threshold": 3000
                    }
                }
            }
        ]
}'

Python

import requests
user_id = "[e-mail]"
auth_token = "replace this with your auth token"
txn_id = "Replace this with your transaction ID"

#Note: the speech to text model does not depends on any other models, hence
#can be used independently

def get_speech_insights():
  url = "https://api.marsview.ai/cb/v1/conversation/{user_id}/compute"
  payload={
        "userId":user_id,
        "txnId": txn_id,
        "enableModels":[
            {
            "modelType":"speech_to_text",
                "modelConfig":{
                    "automatic_punctuation" : True,
                    "custom_vocabulary":["Marsview", "Communication"],
                    "speaker_seperation":{
                        "num_speakers":2
                    },
                    "enableKeywords":True,
                    "enableTopics":False
                }
            },
            {
            "modelType":"emotion_analysis"
            },
            {
            "modelType":"sentiment_analysis"
            },
            {
            "modelType":"data_insights",
            "modelConfig": {
                "dead_air": {
                    "threshold": 3000
                    }
                }
            }
            ]
        }

  headers = {'authorization': '{}'.format(auth_token)}
  
  response = requests.request("POST", url.format(user_id=user_id), headers=headers, json=payload)
  print(response.text)
  if response.status_code == 200 and response.json()["status"] == "true":
    return response.json()["data"]["enableModels"]["state"]["status"]
  else:
    raise Exception("Custom exception")

if __name__ == "__main__": 
    get_speech_insights()

Example Metadata Response

"data":{
        "dataInsights": {
            "meetingInsights": {
                "meetingSentiment": [
                    {
                        "sentiment": "Very Positive",
                        "value": 0.17777777777777778
                    },
                    {
                        "sentiment": "Mostly Positive",
                        "value": 0.15555555555555556
                    },
                    {
                        "sentiment": "Neutral",
                        "value": 0.6666666666666666
                    },
                    {
                        "sentiment": "Mostly Negative",
                        "value": 0
                    },
                    {
                        "sentiment": "Very Negative",
                        "value": 0
                    }
                ],
                "meetingEmotion": [
                    {
                        "emotion": "joy",
                        "value": 0.007523583540714161
                    },
                    {
                        "emotion": "optimism",
                        "value": 0.3097979975693039
                    },
                    {
                        "emotion": "anticipation",
                        "value": 0.2518085595231206
                    },
                    {
                        "emotion": "Misc",
                        "value": 0.2654667631228659
                    },
                    {
                        "emotion": "anger",
                        "value": 0.07488859308987789
                    },
                    {
                        "emotion": "fear",
                        "value": 0.08391689912610677
                    },
                    {
                        "emotion": "sadness",
                        "value": 0.00659760402801088
                    }
                ],
                "conversationStartTime": 1390,
                "engagementRatio": 0.9813153112221717,
                "keywords": [
                    {
                        "keyword": "will",
                        "frequency": 2
                    },
                ],
                "deadAir": 0.015454716272078131
            },
            "speakerInsights": {
                "speakers": [
                    "-1"
                ],
                "speakersTalktimePc": {
                    "-1": 0.007523583540714161
                },
                "speakersTalktime": {
                    "-1": 1300
                },
                "speakersMonologue": {
                    "-1": 13750
                },
                "speakersEmotion": {
                    "-1": [
                        {
                            "emotion": "joy",
                            "value": 0.007523583540714161
                        },
                        {
                            "emotion": "optimism",
                            "value": 0.3097979975693039
                        },
                        {
                            "emotion": "anticipation",
                            "value": 0.2518085595231206
                        },
                        {
                            "emotion": "Misc",
                            "value": 0.2654667631228659
                        },
                        {
                            "emotion": "anger",
                            "value": 0.07488859308987789
                        },
                        {
                            "emotion": "fear",
                            "value": 0.08391689912610677
                        },
                        {
                            "emotion": "sadness",
                            "value": 0.00659760402801088
                        }
                    ]
                },
                "speakersSentiment": {
                    "-1": [
                        {
                            "sentiment": "Very Positive",
                            "value": 0.17777777777777778
                        },
                        {
                            "sentiment": "Mostly Positive",
                            "value": 0.15555555555555556
                        },
                        {
                            "sentiment": "Neutral",
                            "value": 0.6666666666666666
                        },
                        {
                            "sentiment": "Mostly Negative",
                            "value": 0
                        },
                        {
                            "sentiment": "Very Negative",
                            "value": 0
                        }
                    ]
                },
                "speakerAvgWpm": {
                    "-1": 163.55519043739972
                }
            },
            "transcriptInsights": [

                {
                    "sentence": "I am currently attending Nicholas State University to complete my degree in secondary education with a focus on social studies.",
                    "startTime": 56180,
                    "endTime": 64779.999,
                    "speaker": "-1",
                    "topics": [
                        {
                            "tiers": [
                                {
                                    "tierName": "Education",
                                    "type": 1
                                }
                            ],
                            "name": "Secondary Education"
                        },
                    ],
                    "keywords": [
                        "state",
                        "education",
                        "focus",
                        "Nicholas State University"
                    ],
                    "speechType": "statement",
                    "speechTypeConfidence": 0.9999955892562866,
                    "sentiment": "Neutral",
                    "polarity": -0.041666666666666664,
                    "subjectivity": 0.2916666666666667,
                    "tone": "angry",
                    "toneConfidence": 0.7229840755462646,
                    "emotion": "optimism",
                    "emotionConfidence": 0.640371561050415,
                    "wordsPerMinute": 132.57355506454238
                },

            ]
        },
},

Response Objects

Field	Description
dataInsights	Data insights object containing all the insights of the given audio.video
transcriptInsights	List of trabscript insight objects for each sentence identified by the model
meetingInsights	Object containing all the insights of the meeting
speakerInsights	Object containing all the insights of the speaker in the meeting

transcriptInsights List<Objects>

Field	Description
sentence	Sentence Identified in the given time frame
startTime	Start time of the sentence in the input Video/Audio in milliseconds
endTime	End time of the sentence in the input Video/Audio in milliseconds
speaker	Speaker id whose voice is identified in the given time frame
topics	List of topic object identified in the given time frame
keywords	List of keywords found in the given sentence
speechType	The type of speech best representing the sentence identified in the given time frame eg: Statement, Question,
speechTypeConfidence	The models confidence in the predicted speechType
sentiment	Sentiment of the speaker during the given time frame .
polarity	Integer representation of the sentiment of the speaker. Can have values between -1 and 1. -1 being very negative and 1 being very positive.
subjectivity	A scale of how much the sentence is based on facts and figures. A high subjectivity indicates that the information given by the speaker is not based on facts and that it is highly subjective.
tone	Tone of the speaker in the given time frame
toneConfidence	Value indicating the models confidence in the predicted tone value
emotion	Emotion of the speaker in the given time frame.
emotionConfidence	Value indicating the models confidence in the predicted emotion value.
wordsPerMinute	Average words per minute spoken by the speaker in the given time frame.

meetingInsights Object

speakerInsights Object