Audio-visual speech recognition (AVSR) is a multimodal extension of automatic
speech recognition (ASR), using video as a complement to audio. In AVSR,
considerable efforts have been directed at datasets for facial features such as
lip-readings, while they often fall short in evaluating the image comprehension
capabilities in broader contexts. In this paper, we construct SlideAVSR, an
AVSR dataset using scientific paper explanation videos. SlideAVSR provides a
new benchmark where models transcribe speech utterances with texts on the
slides on the presentation recordings. As technical terminologies that are
frequent in paper explanations are notoriously challenging to transcribe
without reference texts, our SlideAVSR dataset spotlights a new aspect of AVSR
problems. As a simple yet effective baseline, we propose DocWhisper, an AVSR
model that can refer to textual information from slides, and confirm its
effectiveness on SlideAVSR.
Audio-visual speech recognition and the use of video in ASR
Audio-visual speech recognition (AVSR) is an advanced form of automatic speech recognition (ASR) that combines video with audio to improve recognition accuracy. While ASR traditionally relies solely on audio information to transcribe speech, AVSR takes advantage of visual cues from the speaker’s face, such as lip movements, to enhance the recognition process.
In recent years, there has been a significant focus on developing datasets for AVSR that specifically capture facial features, particularly lip-readings. However, these datasets often lack broader context evaluation, meaning they don’t effectively assess a model’s ability to comprehend images and visuals in a more holistic manner.
The introduction of SlideAVSR dataset
To address these limitations, the researchers have introduced the SlideAVSR dataset as a new benchmark for AVSR models. This dataset utilizes scientific paper explanation videos as its primary source of data. By transcribing speech utterances while considering the accompanying text on slides in the presentation recordings, SlideAVSR provides a more comprehensive evaluation of AVSR models’ performance.
An important aspect that the SlideAVSR dataset highlights is the challenge of accurately transcribing technical terminologies frequently used in paper explanations. These terms can be particularly difficult to transcribe correctly without reference texts, making this dataset an intriguing addition to the AVSR research landscape.
The baseline model: DocWhisper
As part of their research, the authors have proposed a baseline AVSR model called DocWhisper. This model leverages the textual information available from the slides to assist in transcribing speech. By incorporating this additional data source, DocWhisper aims to improve the accuracy of AVSR systems when dealing with challenging technical terms.
As a simple yet effective baseline model, DocWhisper serves as a starting point for further advancements in AVSR technology. Its successful performance on the SlideAVSR dataset demonstrates the potential of using textual information from slides to enhance AVSR models.
Connections to multimedia information systems and related technologies
The concept of AVSR is closely intertwined with the broader field of multimedia information systems, as it combines both audio and visual data to enable more accurate speech recognition. By incorporating video, AVSR systems can capture additional visual cues that improve recognition accuracy.
Furthermore, AVSR is closely related to other immersive technologies such as animations, artificial reality (AR), augmented reality (AR), and virtual reality (VR). These technologies all involve the manipulation and presentation of multimodal content, including audio and visual elements, to create immersive or interactive experiences.
For example, in AR and VR applications, accurate audio-visual speech recognition is crucial for creating realistic and natural user interactions. By accurately transcribing and understanding speech within these immersive environments, AVSR can enhance the overall user experience and enable more natural human-computer interactions.
Overall, the research into AVSR, as demonstrated by the SlideAVSR dataset and the DocWhisper model, showcases the importance of incorporating multiple modalities in information systems, particularly in the context of multimedia, animations, artificial reality, augmented reality, and virtual realities.