[AI from Scratch] Episode 318: Keyword Spotting — Detecting Specific Keywords in Speech

Recap and Today’s Theme

Hello! In the previous episode, we discussed evaluation metrics for speech recognition systems, such as Word Error Rate (WER), and how these metrics help assess model performance. Today, we will focus on a technology that requires real-time performance: Keyword Spotting (KWS).

Keyword Spotting (KWS) is the process of detecting specific keywords (e.g., “H
ey Siri” or “OK Google”) within an audio stream. KWS is crucial in voice assistants and smart devices, where a system must continuously listen for a specific command to activate.

What is Keyword Spotting?

Keyword Spotting (KWS) is a technique used to detect specific keywords or phrases within a continuous audio stream. This is essential for systems that need to respond to user commands, such as:

• Activating Voice Assistants: For example, saying “Hey Siri” or “OK Google.”
• Voice-Controlled Appliances: Detecting commands like “Turn on the lights.”
• Interactive Voice Response Systems: Responding to specific phrases in automated customer support.

KWS must operate efficiently in real-time, ensuring the system responds promptly when the keyword is spoken.

Steps in Keyword Spotting

1. Feature Extraction: The audio signal is converted into Mel-Frequency Cepstral Coefficients (MFCC) or other acoustic features. These features numerically represent the speech, making it easier for models to analyze.
2. Keyword Detection: The extracted features are processed by a model to determine if the keyword is present. Models can include:

• Deep Learning Models: CNNs, RNNs, or transformer-based models for real-time keyword detection.
• Traditional Acoustic Models: HMMs (Hidden Markov Models) or GMMs (Gaussian Mixture Models) can be used to calculate the likelihood of keyword detection.

1. Activation and Scoring: The model computes the probability of the keyword being present and assigns a score. If the score exceeds a predefined threshold, the system recognizes the keyword and triggers the appropriate action.

Common Architectures for Keyword Spotting

1. Convolutional Neural Networks (CNNs)

CNNs are commonly used for KWS because they are efficient at processing speech features like MFCC or spectrograms. CNN-based models include:

• MobileNet: A lightweight CNN architecture suitable for real-time KWS on mobile or embedded devices.
• ResNet: A deeper architecture that captures more complex patterns, improving keyword detection accuracy.

2. Recurrent Neural Networks (RNNs)

RNNs, especially LSTM or GRU networks, are used in KWS because they capture the temporal dependencies in speech. They are particularly effective for longer audio sequences where keyword location is uncertain.

3. Transformer Models

Transformers, known for their parallel processing capabilities, are gaining popularity in KWS. Models like BERT or Transformer-XL can capture contextual information in speech more effectively, improving detection accuracy in noisy environments.

Example: Implementing Keyword Spotting with CNN in Python

Let’s look at how to implement a simple keyword spotting model using TensorFlow and LibROSA.

1. Install Required Libraries

pip install tensorflow librosa numpy

2. Example Code for Keyword Spotting Model

import tensorflow as tf
import librosa
import numpy as np

# Feature extraction from audio file using MFCC
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 CNN input
    return mfcc

# Define a CNN model for keyword spotting
def build_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(2, activation='softmax')  # Two classes (e.g., 'yes' or 'no')
    ])
    return model

# Initialize and compile the model
input_shape = (13, None, 1)  # Shape for MFCC input
model = build_model(input_shape)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Display model summary
model.summary()

Explanation

• extract_features(): This function loads the audio file and extracts MFCC features, which are then reshaped for input to the CNN.
• build_model(): Defines a CNN architecture for keyword detection. The model outputs two classes (e.g., ‘yes’ or ‘no’).

Challenges and Future of Keyword Spotting

Challenges

• Noise Robustness: KWS systems often struggle in noisy environments, leading to false positives or missed detections. Implementing noise reduction techniques and training with augmented data is critical.
• Energy Efficiency: For devices like smartphones or wearables, lightweight models that consume less power are essential for running KWS systems without draining the battery.

Future Directions

• Self-Supervised Learning: Applying techniques like Wav2Vec could enable more accurate keyword detection using unlabeled data.
• Multilingual Support: Expanding KWS to support multiple languages and dialects is a growing area of research.

Summary

In this episode, we explored Keyword Spotting (KWS), its architecture, and how to implement a simple model using CNNs. KWS is a critical technology for voice assistants and smart devices, requiring real-time performance and high accuracy. In the next episode, we will dive into emotion recognition in speech, where we’ll learn how to analyze emotions from audio data.

Next Episode Preview

Next time, we’ll explore emotion recognition in speech, focusing on techniques for detecting emotions based on vocal characteristics and how these technologies are applied.

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Notes

• MFCC (Mel-Frequency Cepstral Coefficients): A set of features representing speech characteristics, commonly used in speech recognition and keyword spotting.
• MobileNet: A lightweight CNN architecture optimized for mobile devices and embedded systems.