[AI from Scratch] Episode 301: What is Speech Processing? — A Guide to Working with Audio Data

Recap and Today’s Theme

Hello! In the previous episode, we summarized Chapter 10 and conducted a knowledge check to review and deepen our understanding. Now, we’re moving into Chapter 11, where we will learn about speech recognition and speech processing. This chapter will cover the fundamentals of speech data and how to process it, along with the basic and advanced techniques of speech recognition.

In this first episode of Chapter 11, we’ll introduce the concept of speech processing and provide an overview of how speech data is handled. We’ll explore what speech processing is, how audio data is managed, and the basics of technologies like speech recognition and speech synthesis.

What is Speech Processing?

Speech processing refers to the technology that processes and utilizes audio data in various ways. Since speech is originally an analog signal, it must be digitized to handle it as data. Speech processing involves analyzing and processing this data for various applications, including:

• Speech recognition: Converting spoken language into text (e.g., voice assistants or automated subtitles).
• Speech synthesis: Converting text into natural-sounding speech (e.g., navigation systems or text-to-speech software).
• Noise cancellation: Removing background noise from audio data to produce clearer sound.
• Acoustic analysis: Analyzing speech characteristics such as pitch, loudness, and frequency for tasks like emotion analysis or speaker recognition.

Main Applications of Speech Processing

1. Speech Recognition (Automatic Speech Recognition: ASR):

• Converts spoken words into text, enabling technologies like voice commands or conversational understanding when combined with natural language processing (NLP).

1. Speech Synthesis (Text-to-Speech: TTS):

• Converts text into speech, allowing computers to speak like humans. Commonly used in AI assistants or reading software for the visually impaired.

1. Speech Recognition with Emotion Analysis:

• Analyzes emotions from speech, enabling applications in chatbots or customer support systems to adjust responses based on detected emotions.

These technologies are used in smartphones, navigation systems, and even in healthcare and education.

Digitizing Speech Data

Since speech is an analog signal (a continuous waveform), it must be digitized for computers to process. This digitization involves two key steps:

1. Sampling

Sampling refers to measuring the analog speech signal at regular intervals and converting it into numerical data. The sampling rate (measured in Hz) defines how many times per second the sound is sampled. Common sampling rates include 44,100Hz (CD quality) and 16,000Hz (used for speech recognition).

• Higher sampling rate: Better sound quality, but larger data size.
• Lower sampling rate: Smaller data size, but lower quality and reduced analysis accuracy.

2. Quantization

Quantization converts the sampled data into discrete values (bits). The bit depth (number of bits) determines the precision of this conversion. Common bit depths include 16-bit (CD quality) and 8-bit.

• Higher bit depth: Captures more detailed sound but increases data size.
• Lower bit depth: Reduces data size but may introduce noise and reduce sound quality.

By performing these processes, analog audio signals are converted into digital data (Pulse Code Modulation: PCM), which can then be processed by computers.

Techniques and Methods in Speech Processing

There are various techniques used to process and analyze speech data. Below are some of the most commonly used methods:

1. Fast Fourier Transform (FFT)

Fourier Transform is a technique that converts a signal from the time domain to the frequency domain. Since speech consists of various frequency components, breaking it down allows us to analyze its characteristics, such as pitch or tone. FFT is a computationally efficient version of Fourier Transform, widely used in speech recognition and acoustic analysis.

2. Mel-Frequency Cepstral Coefficients (MFCC)

MFCC is a commonly used feature in speech recognition systems. It processes the frequency components obtained through Fourier Transform using the Mel scale, which mimics human hearing perception. This feature extraction enables speech recognition systems to analyze speech more accurately.

3. Speech Filtering

Speech data often contains noise. Filtering techniques remove specific frequency bands of noise, making speech clearer. Filters such as low-pass and high-pass filters are used to eliminate unwanted frequencies.

4. Speech Synthesis (Text-to-Speech: TTS)

Speech synthesis generates speech from text data. In TTS systems, text is broken down into phonemes (basic units of speech), and corresponding waveforms are generated for each phoneme. Recent AI-based systems like WaveNet have made it possible to generate more natural-sounding speech, closely resembling human voices.

Practical Example of Speech Processing

Let’s explore how speech data can be handled using Python. Below is a simple example of reading audio data and extracting features using the librosa library.

Installing Required Libraries

pip install librosa

Python Example: Loading and Analyzing Audio Data

import librosa
import matplotlib.pyplot as plt
import numpy as np

# Load an audio file
audio_path = 'example.wav'
y, sr = librosa.load(audio_path, sr=None)

# Display the waveform
plt.figure(figsize=(10, 4))
librosa.display.waveshow(y, sr=sr)
plt.title('Waveform')
plt.show()

# Extract MFCC features
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
plt.figure(figsize=(10, 4))
librosa.display.specshow(mfccs, sr=sr, x_axis='time')
plt.colorbar()
plt.title('MFCC')
plt.show()

• librosa.load(): Loads the audio file and returns the waveform data and sampling rate.
• librosa.feature.mfcc(): Extracts MFCC (Mel-Frequency Cepstral Coefficients) from the audio data.
• librosa.display.waveshow(): Displays the waveform of the audio file.

This Python example demonstrates how to load and process audio data, extracting features like MFCCs, which are commonly used for speech recognition.

Summary

In this episode, we introduced the basics of speech processing and covered key techniques like speech recognition, synthesis, and noise filtering. By understanding how audio data is digitized and processed, you can apply this knowledge to more advanced tasks such as speech recognition and synthesis.

Next Episode Preview

In the next episode, we will explore the fundamentals of audio data, covering topics like sampling rates and bit depth in more detail. Understanding how to properly handle audio data will deepen your understanding of speech processing.

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Notes

• MFCC: A feature extraction method widely used in speech recognition systems.
• Fourier Transform: A technique for converting signals from the time domain to the frequency domain, essential for analyzing sound characteristics.