Project Description
Case Studies / Speech & Voice AI / Speech Emotion Recognition
Speech & Voice AI
Speech Emotion Recognition for Critical Market Insights
An eight-emotion recognition model that turns survey and focus-group audio into decision-ready sentiment for a leading market research firm.
Talk to our AI teamThe Client
Client Overview
A leading market research firm set out to understand consumer sentiment beyond what surveys capture — building a Speech Emotion Recognition system to analyze audio from customer surveys and focus groups, and surface emotional insight that guides marketing and product decisions.
01 — The Challenge
The Challenge
Emotion is a hard signal to read from raw audio. The system had to meet four requirements before it could be trusted with real decisions.
Predict a speaker's emotion from a single spoken utterance.
Reach high accuracy classifying emotions from speech signals.
Perform evenly across every emotion, avoiding bias toward common ones.
Read conversational context, using prior utterances for sharper judgment.
02 — The Approach
The Approach
A deep-learning pipeline built for balanced, reliable emotion detection: four emotional-speech datasets were combined and balanced, converted to MFCC features, and classified by a convolutional network validated across every emotion class.
Technical implementation — data, model, and evaluation
Data collection & balancing
Four diverse emotional-speech datasets were combined, split into training, validation, and test sets, then augmented with pitch shifting, time stretching, and background noise to even out class distribution.
Feature extraction
Mel-Frequency Cepstral Coefficients (MFCC) were extracted from every clip, converting raw audio into the compact spectral features the model learns from.
Model architecture
A convolutional network with four convolution layers alternating with max-pooling, two dropout layers to control overfitting, and a dense softmax head — trained with Adam and categorical cross-entropy loss.
Training & evaluation
Trained and tested on an NVIDIA A10 GPU, then assessed on accuracy, a full classification report, and a confusion matrix to confirm performance held across every emotion class.
Emotions the model distinguishes
03 — The Impact
The Impact
Beyond the model's 85% accuracy, emotional insight changed how the client made decisions — measured across three business outcomes.
Emotional signal fed directly into strategy calls.
Surfaced sentiment trends surveys had missed.
Emotionally resonant messaging lifted engagement.
How it's measured: tracked over the first six months post-deployment, benchmarked against the client's prior manual analysis workflow.
For the first time we could quantify how customers felt, not just what they said. The emotional signal reshaped how we brief campaigns — and the accuracy held up well enough that the strategy team actually trusts it.
Global market research firm
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