Takaki Makino

Takaki is a Software Engineer on the speech recognition team. Prior to Google, Takaki was working as a project associate professor in Institution of Industrial Science, the University of Tokyo, focusing on a researcher on machine learning and systems theory. Takaki has Doctor of Science from the University of Tokyo.
Authored Publications
Google Publications
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    Preview abstract Traditionally, audio-visual automatic speech recognition has been studied under the assumption that the speaking face on the visual signal is the face matching the audio. However, in a more realistic setting, when multiple faces are potentially on screen one needs to decide which face to feed to the A/V ASR system. The present work takes the recent progress of A/V ASR one step further and considers the scenario where multiple people are simultaneously on screen (multi-person A/V ASR). We propose a fully differentiable A/V ASR model that is able to handle multiple face tracks in a video. Instead of relying on two separate models for speaker face selection and audio-visual ASR on a single face track, we introduce an attention layer to the ASR encoder that is able to soft-select the appropriate face video track. Experiments carried out on an A/V system trained on over 30k hours of YouTube videos illustrate that the proposed approach can automatically select the proper face tracks with minor WER degradation compared to an oracle selection of the speaking face while still showing benefits of employing the visual signal instead of the audio alone. View details
    Basi Garcia
    Brendan Shillingford
    Yannis Assael
    Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop(2019)
    Preview abstract This work presents a large-scale audio-visual speech recognition system based on a recurrent neural network transducer (RNN-T) architecture. To support the development of such a system, we built a large audio-visual (AV) dataset of segmented utterances extracted from YouTube public videos, leading to 31k hours of audio-visual training content. The performance of an audio-only, visual-only, and audio-visual system are compared on two large-vocabulary test sets: an internal set of YouTube utterances (YouTube-AV-Dev-18) and the publicly available TED-LRS3 set. To highlight the contribution of the visual modality, we also evaluated the performance of our system on the YouTube-AV-Dev-18 set artificially corrupted with additive background noise and overlapping speech. To the best of our knowledge, our system significantly improves the state-of-the-art on the TED-LRS3 set. View details
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