In this paper, we explore the learning of neural network embeddings for natural images and speech waveforms describing the content of those images. These embeddings are learned directly from the waveforms without the use of linguistic transcriptions or conventional speech recognition technology. While prior work has investigated this setting in the monolingual case using English speech data, this work represents the first effort to apply these techniques to languages beyond English. Using spoken captions collected in English and Hindi, we show that the same model architecture can be successfully applied to both languages. Further, we demonstrate that training a multilingual model simultaneously on both languages offers improved performance over the monolingual models. Finally, we show that these models are capable of performing semantic cross-lingual speech-to-speech retrieval. Index Terms-Vision and language, unsupervised speech processing, cross-lingual speech retrieval
Recently REF also showed the benefit of joint bilingual training in the domain of speech-to-image and image-to-speech retrieval using English and Hindi data.
Vision as an Interlingua: Learning Multilingual Semantic Embeddings of Untranscribed Speech
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science Engineering