AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary Persons

Automatically generating videos in which synthesized speech is synchronized with lip movements in a talking head has great potential in many human-computer interaction scenarios. In this paper, we present an automatic method to generate synchronized speech and talking-head videos on the basis of text and a single face image of an arbitrary...

Generating Images from Spoken Descriptions

Text-based technologies, such as text translation from one language to another, and image captioning, are gaining popularity. However, approximately half of the world's languages are estimated to be lacking a commonly used written form. Consequently, these languages cannot benefit from text-based technologies. This paper presents 1) a new...

Synthesizing Spoken Descriptions of Images

Image captioning technology has great potential in many scenarios. However, current text-based image captioning methods cannot be applied to approximately half of the world's languages due to these languages’ lack of a written form. To solve this problem, recently the image-to-speech task was proposed, which generates spoken descriptions of...

Show and speak: Directly synthesize spoken description of images

This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes. The basic structure of SAS is an encoder-decoder architecture that takes an image as input and predicts the spectrogram of speech that...

Learning fine-grained semantics in spoken language using visual grounding

In the case of unwritten languages, acoustic models cannot be trained in the standard way, i.e., using speech and textual transcriptions. Recently, several methods have been proposed to learn speech representations using images, i.e., using visual grounding. Existing studies have focused on scene images. Here, we investigate whether fine...

S2IGAN: Speech-to-Image Generation via Adversarial Learning

An estimated half of the world’s languages do not have a written form, making it impossible for these languages to benefit from any existing text-based technologies. In this paper, a speech-to-image generation (S2IG) framework is proposed which translates speech descriptions to photo-realistic images without using any text information, thus...