Drawing insights from Urdu call data.

1. Voice Intelligence (VI)
Analyzing Audio for Better Insights
Tabish Muzaffar
Sobia Aleem
Saad Mughal
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2. Problem Statement
A recent study by Google
has shown that 70% of
mobile users choose ‘call’
as the most preferred
mode of communication
with a business and 61% of
these during their
purchase phase.
But most companies don’t
use this data in their
decision-making process.
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3. PROPOSED SOLUTION
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4. Project Modules
Web Scraping
Speech
Recognition
Sentiment
Analysis
Subject
Categorization
Dashboard
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5. MODULE TECHNICAL DESIGN
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6. Logical Structure
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7. Transcribing Audio
Existing APIs such as the Google Speech API and
IBM Watson API are used to convert audio into
text because of their high accuracy.
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8. Categorization and Sentiment Analysis
Translating text can lead
to increased noise in the
input data that can lead
to a higher rate of error.
Hence, we will be
creating our own Urdu
NLP library – this is the
primary focus and
research area.
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9. Business Intelligence Layer
An existing data visualization tool will be used
for preparing the front-end dashboard because
they are preferred by business owners and
provide key features such as in-memory
analytics and ad-hoc reporting.
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10. WHY DO YOU NEED THIS
SOLUTION?
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11. • Improve customer service
by analyzing their
complaints, feedback, and
queries.
• Cut down on costs of
manual transcription and
human labor.
• Take better decisions by
analyzing customer
sentiments
• Enhance targeted
marketing campaigns by
evaluating what products
customers talk about the
most.
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12. WHAT WILL THE RESULTS LOOK
LIKE?
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NOUN
ADVERB
NEGATIVE
STOPWORD

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15. DEMO
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