Text-to-Speech (TTS) and speech recognition are complementary technologies that address opposite tasks. TTS converts written text into audible speech, enabling devices to "speak" to users. Speech recognition, also called automatic speech recognition (ASR), converts spoken language into text or actionable commands, allowing machines to interpret human speech. The core difference lies in their direction: TTS generates speech output from text, while ASR extracts text or meaning from speech input.
Technically, TTS systems use linguistic rules, phonetic analysis, and machine learning models to synthesize natural-sounding speech. Modern TTS often employs neural networks trained on large datasets of human speech to generate intonation, pacing, and emotion. For example, a TTS system might break down the sentence "It's 3 PM" into phonemes, apply prosody (rhythm and stress), and output audio through a vocoder. Speech recognition systems, conversely, process audio input by breaking it into frames, extracting acoustic features (like Mel-frequency cepstral coefficients), and using acoustic and language models to map sounds to words. For instance, when you say "Set a timer for 5 minutes," ASR identifies phonemes, matches them to words, and applies context to correct ambiguities.
Use cases differ significantly. TTS is used in screen readers (e.g., aiding visually impaired users), voice assistants (e.g., Alexa reading weather updates), and interactive voice response (IVR) systems. Speech recognition powers voice commands (e.g., "Hey Siri"), transcription services (e.g., converting meetings to text), and voice authentication. Challenges also diverge: TTS focuses on achieving naturalness and emotional expressiveness, while ASR prioritizes accuracy in noisy environments and handling accents or dialects. Both rely on machine learning but solve distinct problems in human-machine interaction.