After converting raw audio into a sequence of feature vectors, the next step is to map those features to the basic sounds of a language. This is the primary function of the acoustic model. It addresses the question: given a small segment of audio, what is the probability that a specific phoneme, like /k/, /æ/, or /t/, was spoken?

The acoustic model provides the statistical relationship between the audio signal and its corresponding phonetic units. It computes the likelihood P(\text{audio_features} | \text{phoneme}), a probability that serves as a key input for the final transcription process.

In this chapter, you will cover:

• The function of an acoustic model within the ASR pipeline.
• The traditional approach of using Gaussian Mixture Models (GMMs) to represent audio features for each phoneme.
• How Hidden Markov Models (HMMs) are used to handle the sequential patterns in speech.
• An introduction to how neural networks are used for modern acoustic modeling.

By the end, you will have a clear picture of how this component connects processed sound to the building blocks of speech.