Many approaches to further improve end-to-end speech-to-text translation models have been proposed recently, including our effort on leveraging weakly supervised data. In 2017, we demonstrated that such end-to-end models can outperform cascade models. The emergence of end-to-end models on speech translation started in 2016, when researchers demonstrated the feasibility of using a single sequence-to-sequence model for speech-to-text translation. Dubbed Translatotron, this system avoids dividing the task into separate stages, providing a few advantages over cascaded systems, including faster inference speed, naturally avoiding compounding errors between recognition and translation, making it straightforward to retain the voice of the original speaker after translation, and better handling of words that do not need to be translated (e.g., names and proper nouns). In “ Direct speech-to-speech translation with a sequence-to-sequence model”, we propose an experimental new system that is based on a single attentive sequence-to-sequence model for direct speech-to-speech translation without relying on intermediate text representation. Dividing the task into such a cascade of systems has been very successful, powering many commercial speech-to-speech translation products, including Google Translate. Such systems have usually been broken into three separate components: automatic speech recognition to transcribe the source speech as text, machine translation to translate the transcribed text into the target language, and text-to-speech synthesis (TTS) to generate speech in the target language from the translated text. Speech-to-speech translation systems have been developed over the past several decades with the goal of helping people who speak different languages to communicate with each other. Posted by Ye Jia and Ron Weiss, Software Engineers, Google AI

Google translate voice input