Synthetic-speech researchers ... have been tackling a much tougher challenge: making computers say anything a live person could say, and in a voice that sounds natural.
– from Making Computers Talk. Andy Aaron, Ellen Eide and John F. Pitrelli. Scientific American Explore (March 17, 2003)
TikTok's text-to-speech feature allows creators to put text over their videos and have a Siri-like voice read it out loud. It's a helpful way to annotate your videos to help describe what's happening, add context, or to serve whatever purpose you see fit. There's also no rule saying you can't use it just to make the text-to-speech voice say silly things. Here's how you can easily add text-to-speech to your TikTok videos. You can cancel it, edit the text, or adjust the duration of the text just by tapping the text again. Once you're happy with your video, just click "Next," apply whatever hashtags you want, and post!
We're a couple of decades into the 21st century, cars are literally starting to fly, a vacation to space is just around the corner ... and yet somehow, computers still sound like parodies of confused robots whenever asked to convert text-to-speech (TTS). Come on, devs, there has to be a better solution. A firm called WellSaid Labs believes it has one, and it's getting a boost thanks to an oversubscribed Series A. "Plain and simple, WellSaid is the future of content creation for voice. This is why thousands of customers love using the product daily with off-the-charts bottom-up adoption. Matt and Michael have assembled a world-class team, and we couldn't be more thrilled to be a part of the WellSaid journey," says Cameron Borumand, General Partner at FUSE, which led the round.
Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural speech given text, is a hot research topic in speech, language, and machine learning communities and has broad applications in the industry. As the development of deep learning and artificial intelligence, neural network-based TTS has significantly improved the quality of synthesized speech in recent years. In this paper, we conduct a comprehensive survey on neural TTS, aiming to provide a good understanding of current research and future trends. We focus on the key components in neural TTS, including text analysis, acoustic models and vocoders, and several advanced topics, including fast TTS, low-resource TTS, robust TTS, expressive TTS, and adaptive TTS, etc. We further summarize resources related to TTS (e.g., datasets, opensource implementations) and discuss future research directions.
Vocoders received renewed attention as main components in statistical parametric text-to-speech (TTS) synthesis and speech transformation systems. Even though there are vocoding techniques give almost accepted synthesized speech, their high computational complexity and irregular structures are still considered challenging concerns, which yield a variety of voice quality degradation. Therefore, this paper presents new techniques in a continuous vocoder, that is all features are continuous and presents a flexible speech synthesis system. First, a new continuous noise masking based on the phase distortion is proposed to eliminate the perceptual impact of the residual noise and letting an accurate reconstruction of noise characteristics. Second, we addressed the need of neural sequence to sequence modeling approach for the task of TTS based on recurrent networks. Bidirectional long short-term memory (LSTM) and gated recurrent unit (GRU) are studied and applied to model continuous parameters for more natural-sounding like a human. The evaluation results proved that the proposed model achieves the state-of-the-art performance of the speech synthesis compared with the other traditional methods.
Recently, there has been an increasing interest in neural speech synthesis. While the deep neural network achieves the state-of-the-art result in text-to-speech (TTS) tasks, how to generate a more emotional and more expressive speech is becoming a new challenge to researchers due to the scarcity of high-quality emotion speech dataset and the lack of advanced emotional TTS model. In this paper, we first briefly introduce and publicly release a Mandarin emotion speech dataset including 9,724 samples with audio files and its emotion human-labeled annotation. After that, we propose a simple but efficient architecture for emotional speech synthesis called EMSpeech. Unlike those models which need additional reference audio as input, our model could predict emotion labels just from the input text and generate more expressive speech conditioned on the emotion embedding. In the experiment phase, we first validate the effectiveness of our dataset by an emotion classification task. Then we train our model on the proposed dataset and conduct a series of subjective evaluations. Finally, by showing a comparable performance in the emotional speech synthesis task, we successfully demonstrate the ability of the proposed model.
Xbox's June update is here, and Microsoft has detailed the latest software tweaks Xbox One and Xbox Series X/S users can look forward to trying out on their consoles. To start, the company has officially implemented the speech transcription and text-to-speech synthesis tools it started testing with Xbox Insiders back in May. Now that they're part of the Xbox operating system, you can find both features in the "ease of access" setting tab under the "game and chat transcription." With speech-to-text transcription, your Xbox will transcribe and display what your party says on an adjustable overlay. With text-to-speech, meanwhile, a synthetic voice will read anything you type into party chat.
Reading text aloud is an important feature for modern computer applications. It not only facilitates access to information for visually impaired people, but is also a pleasant convenience for non-impaired users. In this article, the state of the art of speech synthesis is presented separately for mel-spectrogram generation and vocoders. It concludes with an overview of available data sets for English and German with a discussion of the transferability of the good speech synthesis results from English to German language.
What would you want to do if you could generate the voice of your favorite celebrity? Before I get ahead of myself, let me clearly define the objective of this blog. Given text and some voice clips of the desired speaker (say, Beyonce), I want my AI to output an audio clip where Beyonce is speaking the text that I input to this code. So essentially, this is the same Text To Speech (TTS) problem we saw earlier but with an added constraint to output the speech in a particular speaker's voice. In this blog, I share two methods that can complete our task, and I will be comparing these two methods at the end.
As the title suggests, in this blog we are going to learn about text to speech (TTS) synthesis. What is the first bell which rings in your mind when you listen to text to speech? For me, it's Alexa, Google Home, Siri, and many other conversational bots that are on an exponential rise currently. Advances in deep learning research have helped us to generate human-like voices, so let's see how we can use that. I'll start with a few definitions, but if you want to understand these more then read this blog first.
Microsoft has announced that speech transcription and text-to-speech synthesis is coming to Xbox party chat, starting today for Xbox Insiders. The new features will make it easier for players with hearing or speech difficulties to participate in party chat and are part of an Xbox initiative to improve accessibility. Both features can be found in the "ease of access" tab under "game and chat transcription." With speech-to-text transcription, words spoken in a party are converted into text displayed in an adjustable overlay, as shown above. With text-to-speech enabled, anything you type into party text chat will be ready by a synthetic voice to the rest of the party, with a choice of several voices per language.