Mastering Voice AI : From ASR to Emotion AI to Voice Cloning

Master cutting-edge SpeechLMs and build next-generation voice AI applications with end-to-end speech capabilities

What you'll learn
Develop end-to-end speech language models using Python and Transformer architectures.
Master audio feature extraction and tokenization for speech recognition and synthesis.
Build AI for emotion recognition and personalized speech with real-world applications.
Evaluate SpeechLMs with metrics like WER and explore ethical AI design practices.

Requirements
No prior speech AI experience required – beginner-friendly with hands-on guidance!
A computer with Python 3.7+, TensorFlow/PyTorch, and audio libraries (e.g., Librosa).
Basic Python programming (familiarity with loops, functions, and libraries like NumPy).

Description
Transform your understanding of voice AI with this comprehensive course on Speech Language Models (SLMs) - the revolutionary technology that's replacing traditional speech processing pipelines with powerful end-to-end solutions.What You'll Master:Speech Language Models represent the next frontier in AI, moving beyond the limitations of traditional ASR→LLM→TTS pipelines. This course takes you from fundamental concepts to advanced applications, covering everything from speech tokenization and transformer architectures to emotion AI and real-time voice interactions.Why This Course Matters:Traditional speech processing suffers from information loss, high latency, and error accumulation across multiple stages. SLMs solve these problems by processing speech directly, capturing not just words but emotions, speaker identity, and paralinguistic cues that make human communication rich and nuanced.What Makes This Course Unique:Hands-on Learning: Work with state-of-the-art models like YourTTS, Whisper, and HuBERTComplete Pipeline Coverage: From raw audio to deployed applicationsReal-world Applications: Build ASR systems, voice cloning, emotion recognition, and interactive voice agentsLatest Research: Covers cutting-edge developments in the rapidly evolving SLM fieldPractical Implementation: Learn training methodologies, evaluation metrics, and deployment strategiesKey Technologies You'll Work With:Speech tokenizers (EnCodec, HuBERT, Wav2Vec 2.0)Transformer architectures adapted for speech (Whisper , Conformer models etc)Vocoder technologies (Tacotron, Hi-Fi GAN, MelGAN etc)Multi-modal training approaches (CTC, UCTC etcParameter-efficient fine-tuning (LoRA)Perfect For:AI/ML engineers wanting to specialize in speech technologyStudents or Career ChangersResearchers exploring next-generation voice AIDevelopers building voice-first applicationsAnyone curious about how modern voice assistants really workCourse Outcome:By completion, you'll have the skills to design, train, and deploy Speech Language Models for diverse applications - from basic speech recognition to sophisticated emotion-aware voice agents. You'll understand both the theoretical foundations and practical implementation details needed to contribute to this exciting field.Join the voice AI revolution and master the technology that's reshaping human-computer interaction!

Who this course is for:
This course is for aspiring AI developers, data scientists, and tech enthusiasts eager to pioneer the future of voice AI with Speech Language Models., Perfect for beginners with basic Python and ML skills, as well as intermediate learners aiming to build advanced applications like real-time speech recognition, emotion-aware voice assistants, and speech translation., Unlock the power of end-to-end speech processing for cutting-edge careers in AI!