[AI from Scratch] Episode 319: Speech Emotion Recognition — How to Estimate Emotions from Speech

Recap and Today’s Theme

Hello! In the previous episode, we covered keyword spotting (KWS), a technique used to detect specific keywords in real-time, which plays a critical role in voice assistants and smart devices.

Today, we will focus on speech emotion recognition. This technology estimates the emotional state of a speaker from audio signals and is crucial in fields such as customer service improvement and Affective Computing. In this article, we’ll explain how speech emotion recognition works, walk through its implementation, and discuss its applications and challenges.

What is Speech Emotion Recognition?

Speech Emotion Recognition is a technology that identifies the emotional state of a speaker (e.g., joy, anger, sadness) based on the analysis of audio signals. It works by detecting changes in voice modulation, rhythm, pitch, and other factors. Speech emotion recognition is closely related to Natural Language Processing (NLP) and speech recognition, helping to create richer, more intuitive interactions between humans and machines.

Applications of Speech Emotion Recognition

• Customer Support: Real-time analysis of customer emotions to improve service responses.
• Healthcare: Monitoring patients’ emotional states and stress levels.
• Voice Assistants: Providing flexible responses based on user emotions.

How Speech Emotion Recognition Works

The basic process of speech emotion recognition involves extracting features from the speech signal and using machine learning or deep learning models to classify emotions. Below are the main steps:

1. Extracting Speech Features

The first step is extracting features from the input audio data. Common features used in emotion recognition include:

• Fundamental Frequency (F0, Pitch): The pitch of the voice, which can vary depending on emotion. For example, excitement may raise the pitch.
• Energy (Intensity): The loudness of the voice, which tends to be higher in emotional states like anger and lower in calmer states.
• Spectral Features: Features like Mel-Frequency Cepstral Coefficients (MFCC) capture the frequency components of speech, helping to recognize changes in tone and quality.
• Formants: Resonant frequencies created by the vocal tract. These are important for identifying different voice qualities and sounds.

2. Building the Model

Once features are extracted, they are used to train a machine learning or deep learning model. Common techniques include:

• SVM (Support Vector Machine): A machine learning model that separates data into different categories (in this case, emotions). It can work well with smaller datasets.
• Deep Learning Models:
• CNN (Convolutional Neural Networks): Used to process spectrogram-like features such as MFCCs, much like how CNNs process images.
• RNN (Recurrent Neural Networks) and LSTM (Long Short-Term Memory): Ideal for capturing temporal features in speech, as they can maintain long-term dependencies, making them suitable for emotion prediction.

3. Emotion Classification

Once the model is trained, it classifies emotions based on labels like “anger,” “joy,” “sadness,” and “surprise.” The model outputs the most probable emotion based on the extracted features and the context of the speech.

Implementing Speech Emotion Recognition in Python

Let’s look at a simple example of how to implement a speech emotion recognition system using TensorFlow and LibROSA. This implementation will extract MFCC features from audio and use a CNN model to classify emotions.

1. Install the Required Libraries

pip install tensorflow librosa numpy

2. Speech Emotion Recognition Model

Below is an example of a CNN-based model for recognizing emotions from speech:

import tensorflow as tf
import librosa
import numpy as np

# Function to extract MFCC features from an audio file
def extract_features(file_path):
    y, sr = librosa.load(file_path, sr=16000)
    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    mfcc = np.expand_dims(mfcc, axis=-1)  # Reshape for model input
    return mfcc

# Building the CNN model for emotion recognition
def build_emotion_model(input_shape):
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(4, activation='softmax')  # 4 classes: joy, sadness, anger, surprise
    ])
    return model

# Create the model
input_shape = (13, None, 1)  # Shape based on MFCC features
model = build_emotion_model(input_shape)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Display model summary
model.summary()

• extract_features(): Extracts MFCC features from the audio file and reshapes them to fit the CNN input.
• build_emotion_model(): Constructs a CNN architecture tailored for speech emotion recognition, classifying four emotion labels (e.g., “joy,” “sadness,” “anger,” and “surprise”).

This code provides a basic foundation for creating a model capable of learning to recognize emotions from speech data.

Challenges and Future Directions of Speech Emotion Recognition

Challenges

• Noise and Environmental Factors: Background noise and varying recording environments can negatively impact emotion recognition accuracy, necessitating robust noise reduction techniques.
• Lack of Data: Collecting labeled data with emotions can be difficult, and large, diverse datasets are necessary for effective emotion recognition across different speakers and emotions.

Future Directions

• Multilingual Support: There will be more development of emotion models that can handle multiple languages and cultures, understanding emotional expressions across various regions.
• Self-Supervised Learning: By adopting techniques such as Wav2Vec 2.0 and reinforcement learning, speech emotion recognition models can improve even with limited labeled data, opening doors to more accurate and robust systems.

Summary

In this episode, we explored speech emotion recognition, focusing on how to extract emotion-related features from speech and classify emotions using machine learning and deep learning models. Speech emotion recognition is a powerful tool in fields like customer support, healthcare, and voice assistants, helping systems better understand and respond to human emotions. In the next episode, we’ll discuss speaker recognition, a technique for identifying the speaker based on their voice.

Next Episode Preview

In the next episode, we’ll cover speaker recognition, explaining how to identify speakers based on their voice patterns and the underlying technology behind it. We’ll learn how this technology is applied in security systems and personal voice assistants.

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Notes

• MFCC (Mel-Frequency Cepstral Coefficient): A key feature used to capture acoustic characteristics from speech, widely used in speech and emotion recognition.
• CNN (Convolutional Neural Network): A type of neural network effective for processing audio features like spectrograms and MFCCs.