Speech Recognition (ASR)
Automatic Speech Recognition (ASR) is the technology that converts spoken audio into written text. It combines signal processing, acoustic modeling, and language modeling — and today is dominated by end-to-end deep learning architectures such as Transformer-based encoder-decoders (e.g. Whisper), CTC-based models, and self-supervised representations (e.g. wav2vec 2.0). ASR underpins voice assistants, transcription services, real-time captioning, and conversational AI.
In 2026, virtually every consumer-facing AI product integrates a voice layer, making ASR engineers some of the most sought-after specialists at companies like OpenAI, Google, Meta, Microsoft, and a wide range of startups. Demand has widened beyond English to low-resource and multilingual scenarios, and the line between ASR and large language models is blurring — creating cross-functional roles that span acoustics, NLP, and LLM fine-tuning. Teams also need ASR expertise to evaluate, benchmark, and adapt foundation models like Whisper for domain-specific vocabularies and noisy environments.
🎓 Courses
Hugging Face Audio Course
by Hugging Face team
The most up-to-date free course covering ASR end-to-end: audio data processing, Whisper fine-tuning on Common Voice, CTC models, and evaluation. Hands-on with real code throughout.
Open Source Models with Hugging Face
by DeepLearning.AI
Includes a practical ASR unit using the Hugging Face pipeline API, alongside TTS and zero-shot audio classification — ideal for getting hands-on quickly with minimal setup.
ASR with Pipeline (Hugging Face Audio Course, Chapter 2)
by Hugging Face team
Focused deep-dive into running inference with pre-trained ASR models using a simple pipeline abstraction. Excellent entry point before moving to fine-tuning.
Fine-tuning the ASR Model (Hugging Face Audio Course, Chapter 5)
by Hugging Face team
Step-by-step guide to fine-tuning Whisper on Common Voice data. Covers feature extraction, training loop, evaluation with WER, and pushing the model to the Hub.
Speech Recognition Courses
by Various
Coursera aggregates multiple university and industry ASR courses. Useful for finding structured syllabi with certificates, graded assignments, and peer-reviewed projects.
📖 Books
Automatic Speech Recognition: A Deep Learning Approach
Dong Yu and Li Deng · 2015
The canonical technical reference for deep-learning-based ASR. Covers DNN-HMM hybrid models, CTC, sequence discriminative training, and acoustic-language model integration with full mathematical rigour. Still the most cited graduate-level ASR textbook.
🛠️ Tutorials & Guides
Fine-Tune Whisper For Multilingual ASR with Transformers
The go-to practical guide for adapting OpenAI Whisper to new languages or domains. Covers the full pipeline: feature extractor, tokenizer, training with Seq2SeqTrainer, and WER evaluation. Kept up-to-date by the Hugging Face team.
Fine-Tuning Whisper on a Custom Dataset
Concrete walkthrough using air traffic control audio as the domain — a clear example of domain adaptation. Good complement to the HF blog for seeing a non-standard dataset workflow.
Everything You Need to Know About Fine-Tuning an ASR (Focus on Whisper)
Production-oriented guide covering LoRA-based fine-tuning and FlashAttention-2 to reduce GPU requirements. Reflects 2025 best practices for efficient ASR adaptation in enterprise settings.
Learning resources last updated: June 18, 2026