Phonetics
Phonetics scrutinizes the physical properties of speech sounds—their production, transmission, and perception. In computational linguistics, this takes a turn towards analyzing and modeling these sounds using acoustic signal processing.
It is the bedrock for technologies that bridge the gap between biological speech and digital data, determining features like pitch, formant frequencies, and intensity.
Deciphering the Spoken Word
Automatic Speech Recognition (ASR) maps highly variable acoustic signals to discrete words. It tackles challenges like accents, emotional state, and background noise.
- Feature Extraction: Identifying pitch and energy.
- Classification: Fricatives vs. Vowels.
- Deep Learning: Handling coarticulation and variability.
Crafting the Voice of Machines
Text-to-Speech (TTS) systems aim to generate human-like speech. This involves simulating articulation (lip/tongue movement), acoustics (pitch/loudness), and prosody (rhythm/intonation).
Goal
"To harness this understanding to build applications that can interact with humans in their natural language."
ACOUSTIC SIGNAL PROCESSING
SYSTEMATIC ORGANIZATION
Phonology
While phonetics studies the physical sound, Phonology explores the abstract rules and patterns governing how sounds intertwine to form meaning. It is the grammar of sound.
It explains why sounds change in different contexts (assimilation) or disappear (deletion), creating a roadmap for understanding spoken language.
Computational Phonology employs complex models to simulate these rules. It allows for:
- Scalability: Analyzing large corpora of dialects and accents.
- ASR Enhancement: Helping systems handle phonological variations and coarticulation.
- NLP Integration: Assisting in syllabification, stress assignment, and word boundary detection.
With Deep Learning and Big Data, we are moving from explicit rule encoding to neural networks that learn abstract phonological patterns directly from data. This promises more natural synthesis and robust recognition across diverse languages.
Morphology
Morphology is the study of the internal structure of words. It dissects words into morphemes—the smallest meaningful units.
Example: "Unhappiness"
Crucial for translating between languages with different structures (e.g., English vs. German). Systems must understand inflections (tense, case, number) to generate accurate target text.
Example: Recognizing that "ihm" in German implies a dative case (indirect object) and translating the relationship correctly.
Morphological analysis powers Search Engines. It stems words to their roots (e.g., "running" -> "run") so queries match "runner" and "runs". It also handles spelling mistakes and slang normalization.
WORD FORMATION
HIERARCHICAL PARSING
Syntax
Syntax is the architect of sentence structure. Parsing is the computational process of dissecting a sentence into its constituent parts (Parse Trees) to determine grammatical roles.
It unweaves the tapestry of language, allowing machines to understand relationships between words (e.g., who did what to whom).
- Constituency-based Views sentences as nested phrases (Phrase Structure Grammar). Like boxes within boxes.
- Dependency-based Views sentences as a network of words connected by dependencies. The verb is usually the central hub.
Machine Translation: Ensures translated sentences follow the target language's grammatical rules (e.g., word order).
Information Extraction: Identifies relevant information by understanding the structural relationship between entities in unstructured text.
Semantics
Beyond structure lies Meaning. Semantics decodes the denotations (literal) and connotations (implied) of words. It bridges the gap between the word "cat" and the concept of the animal.
Formal Systems
Using logic and algorithms to derive the underlying proposition (truth-value) of a sentence.
Machine Translation
Captures subtleties to ensure translations are idiomatically appropriate, not just literal substitutions.
MEANING & REFERENCE
User
"It's a bit chilly in here."Bot (Pragmatic)
"I'll turn up the heat."CONTEXTUAL INTERPRETATION
Pragmatics
Pragmatics studies how context and speaker intentions shape meaning. It moves from "what is said" to "what is meant".
- Speech Acts: "Could you pass the salt?" is a request, not a yes/no question.
- Implicature: Suggested meanings that aren't explicitly stated.
Essential for Chatbots. A pragmatic-aware system infers underlying intentions. If asked "How are you?", it replies with social convention, not a diagnostic report of its server status.
Improves Sentiment Analysis by detecting sarcasm and irony, which can reverse the literal meaning of a statement.
Discourse Analysis
The broadest scope. Discourse analysis examines how sequences of sentences form coherent narratives, arguments, and conversations. It ensures Coherence (logical flow) and Anaphora (referencing back to previous entities).
Automatic Summarization
Distills the essence of text by recognizing logical bridges and key ideas, not just reducing word count.
Machine Translation
Preserves tone, style, and narrative flow across multiple sentences, handling references that span paragraphs.
GLOBAL COHERENCE