speech to text processing using n l p .pptx

Speech to text processing using NLP

1. Speech-to-Text Using NLP Name: Faisal Alolaiwi Student ID: 431100591 Supervisor: Dr. Khaled Nazim

2. Table of Contents Introduction What is NLP? What is Speech-to-Text? Technical Steps Understanding Text (NLU) Intent & Entities Executing Commands Real-World Applications Challenges Future of Voice Tech Conclusion References

3. Introduction Voice interaction between humans and machines has become natural. We can now ask our devices to help us with just a few words. Let’s explore how this works using NLP and speech technologies.

4. What is NLP Natural Language Processing (NLP) is a subfield of AI that enables machines to understand, interpret, and generate human language. It powers translators, chatbots, sentiment analyzers, and voice assistants.

5. What is Speech-to-Text Speech-to-text is the process of converting spoken language into written text. It involves analyzing audio signals and recognizing patterns to produce accurate transcriptions.

6. Technical Steps The speech-to-text process follows several key steps: Convert speech to digital format Segment the audio into frames Extract features like MFCC Feed features into machine learning models to predict the spoken words

7. Understanding Text (NLU) After transcribing speech to text, Natural Language Understanding (NLU) helps determine what the user actually wants. It involves identifying both the intent behind the sentence and any relevant entities such as time or location. Trained language models are used to analyze the sentence and extract this meaning accurately.

8. Intent & Entities Intent: The user's goal (e.g., ask, command, search) Entities: Key info like time, place, object Trained models identify both to interpret the request accurately.

9. Executing Commands Once the system identifies the intent and entities, it takes appropriate action. Example: If the user says "Play jazz music," the system plays it accordingly.

10. Real-World Applications Voice and NLP technologies are now part of daily life through smart assistants: Alexa (Amazon): Recognizes speech, understands intent, and controls smart home devices or answers questions. Google Assistant: Converts voice to text and provides instant responses, integrates with search and apps. Siri, Bixby, Cortana: All use similar NLP pipelines for voice-based tasks. These systems combine speech recognition, NLU, and cloud services to deliver a seamless user experience.

11. Challenges Dialect and accent variation Background noise Similar-sounding words Ambiguous language Need for constant model updates

12. Future of Voice Tech We expect: More accurate interactions Local dialect recognition Emotion detection Broader use in education, health, transport Improved on-device processing and privacy

13. Conclusion Voice is now a natural way to interact with devices. NLP helps systems understand and respond to speech. Used in assistants like Alexa, Siri, and more. Technology will keep getting smarter and easier. The future of interaction may be voice-driven.

14. References Google AI Blog: Understanding Voice (line 5) IBM Watson Docs: NLP & Speech (page 12) Jurafsky & Martin: NLP Book (Chapter 3, p. 65) TowardsDataScience : Intro to STT (paragraph 2)

15. Thanks for listening