Editing Ai Audio Speech (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Audio is a continuous 1D waveform, typically sampled at 16,000–44,100 Hz. A 10-second speech clip at 16kHz = 160,000 samples — a long sequence. Direct learning from raw waveforms is possible (WaveNet, SoundStream) but computationally expensive. The dominant approach: convert to 2D time-frequency representations (spectrograms), then apply computer vision or sequence models.

'''ASR evolution''': Early ASR combined separate acoustic models (HMMs + Gaussian mixtures) and language models. Deep learning replaced the acoustic model with neural networks but kept the pipeline. Modern end-to-end models (CTC-based, attention-based encoders) jointly learn acoustic-to-linguistic mapping. Whisper uses a transformer encoder-decoder, treating ASR as a sequence-to-sequence translation problem, and achieves remarkable robustness across accents, noise, and languages.

'''Self-supervised audio''': Wav2Vec 2.0 pre-trains on unlabeled audio by learning to identify the correct quantized speech representation for a masked segment among distractors. This contrastive objective learns rich speech representations without transcriptions. Fine-tuning on even 10 minutes of labeled audio achieves competitive ASR — transformative for low-resource languages.

'''TTS pipeline''': Text → phonemes → acoustic features (mel spectrogram) → waveform. Tacotron 2 generates mel spectrograms from phoneme sequences. HiFi-GAN (vocoder) converts mel spectrograms to high-fidelity audio. Modern neural TTS is nearly indistinguishable from human speech in quality evaluations.

'''The voice cloning challenge''': Modern TTS systems like VALL-E can clone a voice from 3 seconds of audio, generating speech in that voice from any text. This enables transformative accessibility applications and severe deepfake risks simultaneously.
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