In this article, I’ll discuss a few things about Speech to Text software and how it can benefit you. It helps you type faster using your voice, accelerate your workflow, enhance your efficiency, and provide rest to your hands. Yes, that’s true, and this technology is Speech to Text software. In this age of automation, it is possible to type with your voice without involving your hands. This means there’s a good scope of saving your time spent on typing stuff. The thing is, writing physically is way slower than the actual processing speed of your brain. In fact, writing is an essential task that everyone needs to do in their professional careers, be it writing an email, blog post, newsletters, and novels to preparing presentations, documenting ideas, taking notes, and whatnot.Įven if you type faster, this speed is still less than the speed while speaking. These solutions bring more efficiency to the table for individuals and businesses alike. The first books were printed in black letter ie the letter which was a gothic development of the ancient roman character 200-Pairs OnlySpeech-to-Text solutions are becoming popular, especially after the advent of voice search services like Alexa. The forms of printed letters should be beautiful and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves 200-Pairs Only Than in the same operations with ugly ones 200-Pairs Only Your browser does not support the audio element.Įspecially as no more time is occupied or cost incurred in casting setting or printing beautiful letters 200-Pairs Only Our method achieves 99.84% in terms of word level intelligible rate and 2.68 MOS for TTS, and 11.7% PER for ASR on LJSpeech dataset, by leveraging only 200 paired speech and text data (about 20 minutes audio), together with extra unpaired speech and text data. Our method consists of the following components: (1) a denoising auto-encoder, which reconstructs speech and text sequences respectively to develop the capability of language modeling both in speech and text domain (2) dual transformation, where the TTS model transforms the text $y$ into speech $\hat,y)$ for training, and vice versa, to boost the accuracy of the two tasks (3) bidirectional sequence modeling, which addresses error propagation especially in the long speech and text sequence when training with few paired data (4) a unified model structure, which combines all the above components for TTS and ASR based on Transformer model. In this paper, by leveraging the dual nature of the two tasks, we propose an almost unsupervised learning method that only leverages few hundreds of paired data and extra unpaired data for TTS and ASR. However, the lack of aligned data poses a major practical problem for TTS and ASR on low-resource languages. Text to speech (TTS) and automatic speech recognition (ASR) are two dual tasks in speech processing and both achieve impressive performance thanks to the recent advance in deep learning and large amount of aligned speech and text data. Yi Ren* (Zhejiang University) Xu Tan* (Microsoft Research) Tao Qin (Microsoft Research) Sheng Zhao (Microsoft) Zhou Zhao (Zhejiang University) Tie-Yan Liu (Microsoft Research) Equal contribution.Paper: Almost Unsupervised Text to Speech and Automatic Speech Recognition Authors Almost Unsupervised Text to Speech and Automatic Speech Recognition
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