Sleep Talk Analyzer: A Deep Dive into Nocturnal Conversations and Dream Interpretation

Abstract

This research evaluates the innovative application of speech recognition technology in analyzing sleep speech. The system transcribes nighttime speech and interprets possible dream meanings in multiple languages using the Web Speech API. Utilizing the Web Speech API, the project aims to reduce dependence on external NLP models such as Google or Whisper AI. This research investigates the role of multilingual support in dream interpretation while also improving transcript analysis accuracy. Sleep speech analysis is viewed as a novel intersection between linguistics and psychology through the application of minimal web-based technology. The proposed system aims to analyze individual’s dream by capturing their nocturnal speech, analyzing it, and providing dream interpretations.

Introduction

Overview

Somniloquy, or sleep speech, refers to individuals talking during sleep. This study explores a lightweight, privacy-focused system using the Web Speech API to record, transcribe, and analyze sleep talk in real time. The goal is to uncover subconscious patterns and potential dream meanings, avoiding the privacy risks and computational load of traditional NLP or cloud-based models.

Key Contributions

1. Literature Review

• Traditional Models: Google Speech-to-Text and Whisper AI offer high accuracy but are cloud-based and raise privacy concerns.

• Offline Models: Provide better privacy but often require large, diverse datasets and custom training.

• Web Speech API: Offers a real-time, browser-based, multilingual, and private alternative with limited hardware requirements.

2. Proposed System

A web-based tool that enables users to:

• Record sleep speech through a simple interface

• Transcribe speech using the Web Speech API

• Translate it into multiple languages

• Analyze symbolic dream meanings

• Access insights through a user dashboard

System Components

1. Voice Recording Module – Captures audio during sleep.

2. Speech Recognition Module – Converts speech to text.

3. Multilingual Support Module – Translates text into multiple languages.

4. Dream Analysis Module – Matches keywords with symbolic dream datasets.

5. User Dashboard – Displays transcripts and interpretation results.

Methodology

• Data Collection: Uses noise reduction and privacy-protected storage.

• Speech-to-Text: Real-time transcription via Web Speech API.

• Multilingual Translation: Enhances global accessibility.

• Dream Interpretation: Applies symbolic datasets and cultural context to analyze dream themes such as "water" or "flight."

Implementation

• Frontend: HTML, CSS, JavaScript.

• Backend: JavaScript processes API interactions.

• Database: Stores symbolic meanings.

• Logic: Rule-based matching for dream interpretation.

Results

• Web Speech API performs well in capturing sleep talk.

• Real-time transcription and multilingual support improve user experience.

• Offers a privacy-focused, accessible alternative to AI models.

• Challenges remain in filtering out background noise effectively.

Conclusion

This research evaluates the feasibility of using the Web Speech API for sleep speech analysis without relying on NLP-based AI models. Future improvements include enhancing speech recognition accuracy, expanding dream interpretation datasets, and incorporating sentiment analysis to refine dream predictions. The system introduces a novel, web-based approach to dream analysis, making dream interpretation more accessible and efficient. Future developments will also include a user feedback mechanism, allowing individuals to personalize and validate dream interpretations, thus refining prediction accuracy and providing more meaningful insights.

References

[1] Web Speech API Documentation - Mozilla Developer Network (MDN) [2] Research on Somniloquy - National Sleep Foundation [3] Speech Recognition in Web Applications - IEEE Transactions on Audio, Speech, and Language Processing

Copyright

Copyright © 2025 Surabhi M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.