Speech Recognition and NLP
Speech-to-text (STT) and text-to-speech (TTS) applications rely on NLP techniques combined with deep learning. Automatic speech recognition (ASR) systems convert spoken language into written text, enabling applications like voice assistants (Siri, Alexa, Google Assistant), transcription services, and voice-controlled devices.
Speech recognition models use spectrogram analysis, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to convert audio signals into text. Transformer-based models like Whisper and DeepSpeech have further improved the accuracy of ASR systems.