LEE-NANGI/Swahili_Classification_Project

# Swahili_Classification_Project # SWAHILI AUDIO PREDICTION: AN AUTOMATIC SPEECH RECOGNITION (ASR) PREDICTIVE MODELING PROJECT ## Introduction Swahili, originating on the East African coast, acted as a significant lingua franca, influenced by interactions between Bantu communities and Arab traders. Thriving city-states like Kilwa and Zanzibar boosted its prominence. In Automatic Speech Recognition (ASR) projects, Swahili's unique phonetics and dialects are a compelling challenge. ASR aims to transcribe spoken Swahili into text, making it useful for transcription and translation. Successful ASR for Swahili requires tailored models that adapt to the language's nuances. This technology plays a vital role in preserving and promoting Swahili's linguistic and cultural heritage, fostering cross-cultural communication and expanding the language's utility. ## Problem Statement Swahili, also known as Kiswahili, has a rich history that spans centuries and is now one of the most widely spoken languages in Africa, with millions of speakers across various countries. Today, Swahili is not only a language of communication but also a symbol of cultural heritage and identity for millions of people in East Africa and beyond. Its history reflects the dynamic nature of language, shaped by trade, migration, colonization, and cultural exchange over the centuries. With the increasing availability of digital audio content in Swahili, AnalytiX Insights aims to develop automated systems that can classify and categorize Swahili audio recordings for various applications, including speech recognition, content recommendation, and language learning tools. ## Main Objective To develop an automated system for converting basic Swahili audio into written text using speech recognition technology. ## Specific Objectives 1. To develop a machine learning model capable of translating Swahili audio recordings. 2. To deploy a model that transcribes the recorded audio files. 3. To provide recommendations …