Speechtech

Speechtech refers to technologies that enable automatic processing and generation of human speech. It encompasses automatic speech recognition (ASR) that converts spoken language to text, text-to-speech synthesis (TTS) that converts text to spoken voice, voice biometrics for speaker recognition and verification, and related components such as speaker diarization, speech enhancement, and noise reduction. It relies on signal processing, machine learning, and natural language processing to interpret, translate, and synthesize speech.

Applications include voice-activated assistants, transcription services, customer-service automation (IVR), accessibility tools for visually impaired or dyslexic users, language learning, broadcasting, and automotive voice interfaces. In business, speechtech is used to analyze customer sentiment, route calls, or generate captions for media. It spans on-device and cloud-based implementations, with cross-language and multilingual capabilities.

Development has progressed from template-based and statistical models to end-to-end neural approaches, with deep learning enabling real-time recognition and natural-sounding synthesis. Trends include on-device processing for privacy, multilingual models, emotion and prosody modeling, speaker adaptation, and ethical concerns such as bias, misuse, and consent. Evaluation metrics include word error rate for ASR and mean opinion score for TTS. Standards and benchmarks continue to evolve with open datasets and shared tasks.