Speech And Language Processing [SAFE]

"I saw a man on a hill with a telescope." Who has the telescope? The man, the hill, or the speaker? Humans use common sense to infer meaning. Machines use statistics; they often guess wrong.

Once the speech becomes text, the real work begins. NLP must deal with ambiguity. A single word can have multiple meanings (polysemy). A sentence can be sarcastic. Language is a code that requires vast world knowledge to crack.

SLP is generally divided into three major areas of operation: SPEECH AND LANGUAGE PROCESSING Speech and Language Processing

Humans don't just exchange facts; we exchange emotion. A flat robotic voice saying "That's great" is useless. A human saying "That's great " (with a sneer) means the opposite. Current models struggle to encode the pragmatic intent hidden in pitch contour and facial expression.

To process language effectively, systems must handle several levels of linguistic information: "I saw a man on a hill with a telescope

The structural relationships and rules that govern how words form sentences.

Despite recent hype, remains an unsolved problem. We have passed the Turing Test for specific tasks, but human-level fluency remains elusive due to these hurdles: Machines use statistics; they often guess wrong

Key components of speech processing include:

We are currently entering the era.

| 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 |