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Speech Recognition & Language Processing
Xcel Learning

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Summary

Price
£19 inc VAT
Study method
Online, On Demand 
Duration
1.2 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
Assessment details
  • Review Questions and Assessments (included in price)
Additional info
  • Tutor is available to students

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Overview

Speech Recognition & Language Processing course provides a comprehensive exploration of speech recognition and natural language processing, integrating linguistic theory, signal processing, probabilistic modeling, and deep learning. Students will examine how spoken language is transformed from acoustic signals into meaningful textual and semantic representations. The course covers foundational concepts such as phonetics, morphology, syntax, probability theory, and language modeling, followed by advanced topics including Hidden Markov Models, neural networks, end-to-end architectures, dialogue systems, and speech synthesis. Emphasis is placed on both theoretical understanding and practical system design, enabling learners to analyze, build, and evaluate modern speech-driven applications. Ethical considerations, multilingual challenges, robustness, and emerging trends such as foundation models and multimodal learning are also addressed. By the end of the course, students will possess the knowledge and technical insight required to develop intelligent, scalable, and responsible speech and language technologies.

Certificates

Assessment details

Review Questions and Assessments

Included in course price

Curriculum

13
sections
13
lectures
1h 15m
total

Description

Exciting Journey Ahead: Discover What Awaits in This Course!

Chapter 1: Introduction to Speech and Language Processing

  1. Overview of Speech Recognition Systems
  2. History and Evolution of NLP and ASR
  3. Applications of Speech and Language Technologies
  4. Challenges in Spoken Language Understanding
  5. Architecture of End-to-End Speech Systems

Chapter 2: Fundamentals of Linguistics for NLP

  1. Phonetics and Phonology
  2. Morphology and Word Formation
  3. Syntax and Grammatical Structures
  4. Semantics and Meaning Representation
  5. Pragmatics and Discourse Analysis

Chapter 3: Probability and Information Theory

  1. Probability Theory Refresher
  2. Random Variables and Distributions
  3. Bayes’ Theorem and Bayesian Inference
  4. Information Theory and Entropy
  5. Maximum Likelihood and MAP Estimation

Chapter 4: Text Processing and Language Modeling

  1. Text Normalization and Tokenization
  2. N-gram Language Models
  3. Smoothing Techniques
  4. Evaluation Metrics for Language Models
  5. Neural Language Models

Chapter 5: Acoustic Phonetics and Speech Production

  1. Human Speech Production Mechanism
  2. Acoustic Properties of Speech Signals
  3. Spectrograms and Formants
  4. Coarticulation and Prosody
  5. Speech Perception

Chapter 6: Speech Signal Processing

  1. Digital Signal Processing Basics
  2. Sampling and Quantization
  3. Fourier Transform and Spectral Analysis
  4. Feature Extraction (MFCC, PLP)
  5. Voice Activity Detection

Chapter 7: Hidden Markov Models and Classical ASR

  1. Markov Chains and HMM Fundamentals
  2. HMM for Speech Recognition
  3. Viterbi Algorithm
  4. Baum-Welch Algorithm
  5. Gaussian Mixture Models

Chapter 8: Deep Learning for Speech Recognition

  1. Neural Network Basics
  2. CNNs and RNNs for Speech
  3. LSTM and GRU Architectures
  4. Connectionist Temporal Classification (CTC)
  5. Attention-based Encoder-Decoder Models

Chapter 9: End-to-End Speech Recognition Systems

  1. Transformer Models for ASR
  2. Self-Supervised Learning (e.g., Wav2Vec)
  3. Multilingual and Low-Resource ASR
  4. Streaming and Real-Time Recognition
  5. Evaluation Benchmarks (WER, CER)

Chapter 10: Natural Language Understanding

  1. Part-of-Speech Tagging
  2. Named Entity Recognition
  3. Dependency and Constituency Parsing
  4. Semantic Role Labeling
  5. Intent Detection and Slot Filling

Chapter 11: Dialogue Systems and Speech Applications

  1. Task-Oriented Dialogue Systems
  2. Conversational Agents and Chatbots
  3. Speech Synthesis (TTS)
  4. Multimodal Interaction
  5. Ethics and Bias in Speech Systems

Chapter 12: Advanced Topics and Research Trends

  1. Large Language Models in Speech Processing
  2. Speech Translation Systems
  3. Emotion and Speaker Recognition
  4. Robustness and Adversarial Attacks
  5. Future Directions in Speech and Language Processing

Unleash Your Potential: Join Us Today and Elevate Your Skills with a Prestigious Digital Certificate upon Course Completion!

Who is this course for?

This course is designed for students, researchers, and professionals interested in speech recognition and natural language processing. It suits those with a background in computer science, linguistics, or data science who want to build intelligent systems, as well as developers aiming to create voice-enabled applications and improve human-computer interaction technologies.

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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