The growing prevalence of online conferences and courses presents a new
challenge in improving automatic speech recognition (ASR) with enriched textual
information from video slides. In contrast to rare phrase lists, the slides
within videos are synchronized in real-time with the speech, enabling the
extraction of long contextual bias. Therefore, we propose a novel long-context
biasing network (LCB-net) for audio-visual speech recognition (AVSR) to
leverage the long-context information available in videos effectively.
Specifically, we adopt a bi-encoder architecture to simultaneously model audio
and long-context biasing. Besides, we also propose a biasing prediction module
that utilizes binary cross entropy (BCE) loss to explicitly determine biased
phrases in the long-context biasing. Furthermore, we introduce a dynamic
contextual phrases simulation to enhance the generalization and robustness of
our LCB-net. Experiments on the SlideSpeech, a large-scale audio-visual corpus
enriched with slides, reveal that our proposed LCB-net outperforms general ASR
model by 9.4%/9.1%/10.9% relative WER/U-WER/B-WER reduction on test set, which
enjoys high unbiased and biased performance. Moreover, we also evaluate our
model on LibriSpeech corpus, leading to 23.8%/19.2%/35.4% relative
WER/U-WER/B-WER reduction over the ASR model.
The Importance of Enriched Textual Information in Online Conferences and Courses
With the growing prevalence of online conferences and courses, there is a need for improved automatic speech recognition (ASR) systems that can effectively process and understand the enriched textual information from video slides. Traditional ASR systems mainly rely on rare phrase lists, but the slides within videos provide real-time synchronization with the speech, offering valuable long-context bias. This long-context information can greatly enhance the accuracy and contextual understanding of ASR systems.
The proposed long-context biasing network (LCB-net) for audio-visual speech recognition (AVSR) addresses this need by leveraging the long-context information available in videos. The LCB-net adopts a bi-encoder architecture that simultaneously models audio and long-context biasing. This approach allows for the extraction of contextual bias from the video slides, aiding in accurate speech recognition.
In addition to the bi-encoder architecture, the LCB-net also incorporates a biasing prediction module. This module uses binary cross entropy (BCE) loss to explicitly determine biased phrases in the long-context biasing. By identifying and leveraging biased phrases, the LCB-net further improves the accuracy and performance of ASR systems.
Another important aspect of the LCB-net is the dynamic contextual phrases simulation. This simulation enhances the generalization and robustness of the model by simulating various contextual scenarios and ensuring that the system is capable of handling different speech patterns and contexts.
Multi-disciplinary Nature and Relation to Multimedia Information Systems
The concepts presented in this article highlight the multi-disciplinary nature of multimedia information systems. The LCB-net combines elements from audio processing, computer vision, natural language processing, and machine learning to develop an effective AVSR system. The integration of these different disciplines allows for a comprehensive approach to speech recognition, taking into account both audio and visual cues along with contextual bias from video slides.
Furthermore, the LCB-net’s performance on the SlideSpeech corpus demonstrates its effectiveness in processing and understanding multimedia information. By leveraging the synchronized audio and video slides, the LCB-net outperforms general ASR models. This indicates the relevance of the concepts discussed in this article to the wider field of multimedia information systems.
Relation to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts presented in this article, particularly the use of synchronized audio and video slides, have implications for animations, artificial reality, augmented reality, and virtual realities. In these fields, the combination of audio and visual elements is crucial for creating immersive and interactive experiences.
By leveraging contextual bias from video slides, the LCB-net can enhance the accuracy and understanding of speech in these environments. This can be particularly useful in applications where users interact with multimedia content and need accurate speech recognition, such as virtual reality simulations or augmented reality experiences with voice-controlled interfaces.
In conclusion, the proposed LCB-net offers a promising approach to improving automatic speech recognition in the context of online conferences and courses. Its ability to leverage long-context information from video slides showcases the importance of enriched textual information in multimedia systems. The multi-disciplinary nature of the concepts discussed in this article highlights their relevance to the wider field of multimedia information systems, as well as their potential applications in animations, artificial reality, augmented reality, and virtual realities.