Introduction:

Audio and speech analysis have become critical areas in the advancement of artificial intelligence (AI), driving innovations in voice recognition, sentiment analysis, language translation, and more. This 5-day course provides in-depth knowledge of the techniques and tools used in AI-powered audio and speech analysis. Participants will learn how to apply machine learning and deep learning methods to process, analyze, and understand audio and speech data. With practical hands-on sessions and real-world case studies, this course will enable professionals to build and deploy AI systems for various applications such as speech-to-text conversion, speaker identification, emotion recognition, and sound event detection.

Objectives:

By the end of this course, participants will:

• Understand the fundamental concepts of audio and speech processing.
• Learn how to preprocess and extract features from audio signals, including spectrograms, MFCCs (Mel Frequency Cepstral Coefficients), and pitch.
• Gain hands-on experience with speech-to-text models, such as deep neural networks (DNNs) and recurrent neural networks (RNNs).
• Explore advanced techniques for speaker identification, emotion recognition, and sound event classification.
• Learn how to evaluate and improve the performance of audio and speech models.
• Be equipped to apply AI for real-time audio analysis in applications like virtual assistants, voice-controlled devices, and multimedia content analysis.

Who Should Attend:

This course is ideal for:

• Data Scientists, Machine Learning Engineers, and AI Researchers who want to specialize in audio and speech analysis.
• Professionals working in industries like healthcare, customer service, or media, where audio and speech data is key to improving user experience and operational efficiency.
• Developers looking to integrate speech recognition or emotion detection into their applications.
• Researchers and students interested in the fields of natural language processing (NLP), audio processing, and AI.

Day 1: Introduction to Audio and Speech Analysis

• Morning:
  • Overview of Audio and Speech Analysis:
    • Importance of audio and speech processing in AI applications.
    • Real-world applications: Virtual assistants, transcription, sentiment analysis, and accessibility tools.
  • Types of Audio Data:
    • Audio signals: time-domain and frequency-domain signals.
    • Understanding different audio formats: WAV, MP3, and FLAC.
    • Sampling rate, bit depth, and channels in audio data.
• Afternoon:
  • Fundamentals of Speech Processing:
    • Basic concepts of speech signals: pitch, timbre, and duration.
    • Speech production and perception models.
    • Audio feature extraction: Mel spectrogram, MFCC, and Chroma features.
  • Hands-on Session:
    • Preprocessing and visualizing audio signals (e.g., spectrograms and waveforms).
    • Feature extraction techniques: MFCCs and spectrograms using Python libraries like librosa and pyAudioAnalysis.

Day 2: Speech Recognition and Natural Language Processing (NLP)

• Morning:
  • Speech-to-Text Systems:
    • Introduction to automatic speech recognition (ASR).
    • Traditional ASR techniques: HMMs (Hidden Markov Models) and GMMs (Gaussian Mixture Models).
    • Modern deep learning-based ASR: RNNs, CNNs, and Transformer models.
  • Deep Learning for Speech Recognition:
    • Overview of deep neural networks (DNNs) in speech recognition.
    • Using RNNs (LSTMs) and CNNs for speech-to-text applications.
    • Language models and their importance in improving speech recognition accuracy.
• Afternoon:
  • End-to-End Speech Recognition with Deep Learning:
    • Implementing a deep learning-based ASR model (e.g., using RNNs or Transformer networks).
    • Training and evaluating speech-to-text models on public datasets like LibriSpeech or CommonVoice.
  • Hands-on Session:
    • Building and training an end-to-end ASR model using Keras/TensorFlow or PyTorch.
    • Testing the ASR model on real-world audio clips for transcription.

Day 3: Speaker Identification and Emotion Recognition

• Morning:
  • Speaker Identification and Diarization:
    • Introduction to speaker recognition: speaker identification vs. speaker verification.
    • Feature extraction for speaker identification: Mel spectrograms, pitch, and voiceprints.
    • Techniques for speaker diarization: clustering, VAD (Voice Activity Detection), and UMAP (Uniform Manifold Approximation and Projection).
• Afternoon:
  • Emotion Recognition in Speech:
    • Understanding emotional tone in speech: pitch, speech rate, volume, and intensity.
    • Using audio features to classify emotions: happy, sad, angry, etc.
    • Overview of models for emotion recognition: CNNs, RNNs, and hybrid models.
  • Hands-on Session:
    • Implementing speaker identification models using pre-trained models and datasets like VoxCeleb.
    • Building an emotion recognition system using audio features and deep learning models on a speech dataset like RAVDESS or TESS.

Day 4: Advanced Techniques in Audio and Speech Analysis

• Morning:
  • Sound Event Detection (SED):
    • Overview of sound event detection: identifying and classifying environmental sounds in audio.
    • Using machine learning for SED: features and models for detecting specific sounds (e.g., sirens, animal sounds).
    • Data labeling and training sound event classifiers.
• Afternoon:
  • Speech Synthesis and Text-to-Speech (TTS):
    • Introduction to TTS: converting text into human-like speech.
    • Methods in TTS: concatenative synthesis vs. parametric synthesis.
    • Modern approaches in TTS: Tacotron and WaveNet models.
  • Hands-on Session:
    • Implementing a simple sound event detection model using libraries like PyTorch or Keras.
    • Exploring TTS systems with pre-trained models and generating synthetic speech.

Day 5: Real-World Applications and Case Studies

• Morning:
  • Voice-Activated Assistants:
    • Building virtual assistants (e.g., Alexa, Google Assistant) with speech recognition, natural language understanding (NLU), and text-to-speech (TTS).
    • Integrating voice interfaces with machine learning pipelines.
    • Real-time speech recognition and command processing.
• Afternoon:
  • Case Study 1: Healthcare Applications:
    • Voice-driven diagnostic tools: speech-based medical diagnosis and symptom checkers.
    • Analyzing patient sentiment and emotional state in healthcare calls.
  • Case Study 2: Customer Service Applications:
    • AI in customer service: call center automation and sentiment analysis for customer feedback.
    • Improving customer experience through emotion recognition and speech analysis.
• Final Hands-On Project:
  • Building a comprehensive voice-enabled application that includes speech recognition, emotion detection, and a response system.
  • Presenting the project with a live demo showcasing real-world use cases.

Key Takeaways:

• Strong understanding of audio and speech data processing, including feature extraction and signal processing techniques.
• Practical experience with deep learning models for speech recognition, speaker identification, and emotion analysis.
• The ability to apply AI techniques in real-world applications, such as virtual assistants, healthcare, and customer service.
• Knowledge of the latest trends in audio and speech technologies and their ethical and practical implications.