Project Title: Speech-to-Text Converter

Objective:

The Speech-to-Text Converter project focuses on building a system that automatically converts spoken language into written text using machine learning and deep learning techniques. This has applications in transcription services, voice assistants, accessibility tools, and real-time communication systems.

Key Components:

Problem Statement:

Convert audio recordings or live speech into accurate, readable text.

Handle various accents, background noise, and different languages or dialects.

Data Collection:

Speech Datasets: Public datasets like LibriSpeech, Mozilla Common Voice, TED-LIUM, or custom-recorded audio files.

Each audio file is paired with its corresponding transcript for supervised learning.

Preprocessing:

Noise Reduction and Silence Removal.

Convert audio to spectrograms or Mel-frequency cepstral coefficients (MFCCs).

Normalize audio length and sample rates.

Modeling Techniques:

Traditional Approaches: Hidden Markov Models (HMMs) with Gaussian Mixture Models (GMMs).

Deep Learning Models:

RNNs/LSTMs/GRUs for sequential modeling of audio features.

CNNs for feature extraction from spectrograms.

CTC (Connectionist Temporal Classification) loss to align input and output sequences.

Transformer-based Models like Wav2Vec 2.0, DeepSpeech, or Whisper by OpenAI.

Training & Evaluation:

Train the model on paired audio-transcript data.

Evaluate with Word Error Rate (WER) and Character Error Rate (CER).

Test on unseen audio samples with diverse speakers and noise levels.

Deployment:

Integrate into a web or mobile app using APIs (e.g., Flask or FastAPI).

Provide live or batch transcription.

Enable speaker diarization or language translation optionally.

Applications:

Voice Assistants (e.g., Siri, Alexa).

Meeting Transcripts and Subtitling.

Accessibility Tools for hearing-impaired users.

Call Center Automation and Customer Support Logs.

Challenges:

Dealing with diverse accents and speech rates.

Background noise and overlapping speech.

Real-time latency constraints in live settings.

Outcome:

A functional, accurate speech-to-text system capable of transcribing spoken language into text with minimal errors, supporting multiple use cases in real-world applications.