Advanced Acoustic Modeling for ASR
Acoustic modeling forms the core of an Automatic Speech Recognition (ASR) system, responsible for mapping the input audio features to linguistic units like phonemes or characters. Building upon the foundational concepts from the previous chapter, we now examine the sophisticated deep learning architectures that power state-of-the-art ASR.
This chapter details the architectures and training methodologies for modern acoustic models. You will learn about:
- Hybrid HMM-DNN Systems: Understanding how Deep Neural Networks integrate with Hidden Markov Models.
- End-to-End Models: Implementing and analyzing architectures that directly map speech to text, including Connectionist Temporal Classification (CTC), attention-based encoder-decoders, RNN Transducers (RNN-T), and Transformer-based models.
- Advanced Training Strategies: Techniques like SpecAugment and multi-task learning to improve model generalization.
- Decoding Algorithms: Comparing different search strategies like beam search used with these various model types.
We will analyze the mathematical formulations, implementation details, and trade-offs associated with each approach. The chapter includes a hands-on section where you will build and train an end-to-end ASR model, putting these concepts into practice.
Sections
2.1 Hybrid HMM-DNN Systems
2.2 Connectionist Temporal Classification (CTC)
2.3 Attention-Based Encoder-Decoder Models
2.4 RNN Transducer (RNN-T)
2.5 Transformer Architectures for ASR
2.6 Advanced Training Techniques
2.7 Decoding Algorithms Comparison
2.8 Hands-on Practical: Building an End-to-End ASR Model
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