Speech And Language Processing Guide

Think of it as a two-step pipeline. First, you convert audio into text (Automatic Speech Recognition). Then, you figure out what that text means (Natural Language Understanding). Finally, to close the loop, you often generate a text response and convert it back into audio (Text-to-Speech).

The evolution of Speech and Language Processing is a journey from rigid, hand-crafted rules to fluid, learned representations. Speech and Language Processing

At its core, is the computational study of how to design systems that can recognize, understand, synthesize, and manipulate human language. Think of it as a two-step pipeline

The field is traditionally divided into two major pillars that work in tandem to create seamless human-machine interaction: Finally, to close the loop, you often generate

| Week | Topic | |------|-------| | 1 | Introduction + Regular expressions | | 2 | N-gram LM + Smoothing | | 3 | POS tagging & HMMs | | 4 | Word embeddings (static) | | 5 | Transformers + BERT/GPT | | 6 | CFGs + PCFGs | | 7 | Dependency parsing | | 8 | Semantics (FOL, AMR, WSD) | | 9 | Coreference + Discourse | | 10 | ASR (MFCCs + HMM-DNN/End-to-end) | | 11 | TTS (Tacotron + WaveNet) | | 12 | Machine Translation + LLMs | | 13 | Dialogue systems | | 14 | Ethics + Final project presentations |