#audio-processing

Build speech recognition systems using deep learning for automatic speech recognition and audio processing applications. Audio preprocessing: 1. Signal processing: sampling rate 16kHz, windowing (Hamming, Hann), frame size 25ms, frame shift 10ms. 2. Feature extraction: MFCC (13 coefficients), log-mel filterbank, spectrograms, delta features. 3. Noise reduction: spectral subtraction, Wiener filtering, voice activity detection. Deep learning architectures: 1. Recurrent networks: LSTM/GRU for sequential modeling, bidirectional processing, attention mechanisms. 2. Transformer models: self-attention for audio sequences, positional encoding, parallel processing. 3. Conformer: convolution + transformer, local and global context modeling, state-of-the-art accuracy. End-to-end systems: 1. CTC (Connectionist Temporal Classification): alignment-free training, blank symbol, beam search decoding. 2. Attention-based encoder-decoder: seq2seq modeling, attention mechanisms, teacher forcing. 3. RNN-Transducer: streaming ASR, online decoding, real-time transcription. Language modeling: 1. N-gram models: statistical language modeling, smoothing techniques, vocabulary handling. 2. Neural language models: LSTM, Transformer-based, contextual understanding. 3. Shallow fusion: LM integration during decoding, score interpolation, beam search optimization. Advanced techniques: 1. Data augmentation: speed perturbation, noise addition, SpecAugment for robustness. 2. Multi-task learning: ASR + speaker recognition, emotion recognition, shared representations. 3. Transfer learning: pre-training on large datasets, fine-tuning for specific domains. Evaluation: Word Error Rate (WER <5% excellent), Real-Time Factor (RTF <0.1), confidence scoring, speaker adaptation for improved accuracy.