The previous chapter focused on acoustic modeling, the process of mapping audio signals to sequences of phonetic units or characters. However, achieving high-accuracy speech recognition often requires more than just acoustic evidence. The likelihood of the resulting word sequence, $P(W)$, and the system's ability to handle variations in speech are equally significant. An ASR system aims to find the most probable word sequence $W$ given the acoustic input $X$, often represented as maximizing $P(W|X)$. Language models help estimate $P(W)$, and adaptation techniques help the model generalize better across different $X$.

This chapter introduces methods to integrate linguistic constraints and handle variability. We will cover:

• Using neural network based language models (RNN-LMs, Transformer-LMs) to predict likely word sequences and integrating them during decoding.
• Techniques such as shallow fusion and deep fusion to combine information from the acoustic model and the language model.
• Speaker adaptation methods (including i-vectors and neural approaches) to adjust models for individual voice characteristics.
• Strategies for environment and channel adaptation to improve performance in noisy conditions or with different microphones.
• Leveraging unlabeled audio data using unsupervised and semi-supervised learning.
• Building systems for multi-lingual or cross-lingual scenarios.
• Applying these concepts through practical fine-tuning exercises.