Multimodal Speech Recognition: Difference between revisions
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== Future Research == | == Future Research == | ||
In this section, we propose several directions for future research. To begin with, in terms of databases, it is necessary to design and build large-scale databases for low-resource languages. Despite AVSR being a data-driven technology, audio-visual databases for low-resource languages are very rare, which means training and developing advanced AVSR for those languages are limited. Currently, the dominant source language of large-scale datasets is still English, followed by Chinese, Russian, Arabic and a few other European languages. Moreover, researchers in the future could also work on improving databases’ quality from various aspects (Xia et al., 2020). To name a few, build publicly easily accessible databases for general purposes, build databases that have multiple recording angles, and record audio and video under high quality. | |||
With regard to multimodal, it may be possible that future research does not be limited to bimodal, i.e. visual and audio; it could be a trimodal, or even a true multimodal as “sight - listening - touching - tasting - smelling” (Xia et al., 2020). One previous research has already suggested a trimodal including audio, visual and aero-tactile information for speech perception under a noisy background (Derrick et al., 2019). In this study, air puff was added to audiovisual stimuli /pa/ and /ba/ and the matched pairs (e.g. /pa/ with air puff) had higher speech clarity than mismatched pairs, represented by the decreased SNR that listeners needed for matched pairs. With the ability of machine learning and deep learning, it might just be a matter of time to discover how to extract, represent and fuse multimodal features to current speech recognition modal. | |||
What’s more, researchers could explore if multimodal speech recognition could be applied to a wider domain. It is classic that automatic speech recognition is used for healthcare purposes such as transcribing clinical notes by Nuance and recognizing whisper speech from patients by Whispp, but with the help of an extra modality i.e. visual information, it seems new to use audiovisual speech recognition in forensic fields. A good case would be that an audiovisual speech recognition was used to transcribe audio-visual speech materials and detect child abuse (Vásquez-Correa & Álvarez Muniain, 2023). | |||
Finally, novel deep learning architectures might also be worth implementing in future research. These could be new modals that better integrate features among different modalities and thus could improve the performance of speech recognition. For example, the first Hybrid CTC/Attention architecture for audio-visual recognition and this architecture led to a decrease in word error by 1.3% (Petridis et al., 2018). Some new approaches are also mentioned in other studies, for example, integration between DNN-HMM and MSHMM (Noda et al., 2015). | |||
Revision as of 20:16, 18 September 2023
Weixi Lai
Xueying Liu
Weihao Jiang
Ting Zhang
Introduction
Speech perception by humans is a multi-channel process. People perceive speech not only through hearing but also via other channels, among which the visual channel, particularly lip movements, has a prominent influence. A famous McGurk effect has well demonstrated the effect of visual information. When hearing the sound /ba/ while seeing the lip movement /ga/, many people perceive the sound as /da/. Numerous studies have also proved that lip movements help listeners better disambiguate sounds in a noisy environment and clean environment (Mroueh et al., 2015).
Inspired by the multimodal speech perception of humans, automatic speech recognition (ASR) adopts the multimodal mode as well. It means that ASR is not trained solely on acoustic data; it is trained based on integrated data from various modalities, e.g. combination of acoustic and visual data. Multimodal ASR has become a hot topic these years due to its better recognition performance compared with unimodal ASR. In this page, we briefly introduce its development throughout history, some key innovations and impacts, as well as a few future research ideas.
Historical Context
Key Innovations
Impact
Training audio recognition and visual recognition separately and subsequently linking the two together resulted in lower phone error rates. however, when a new bilinear DNN network was used, which allowed training both audio and visual, the result was to achieve even lower error rates .
Future Research
In this section, we propose several directions for future research. To begin with, in terms of databases, it is necessary to design and build large-scale databases for low-resource languages. Despite AVSR being a data-driven technology, audio-visual databases for low-resource languages are very rare, which means training and developing advanced AVSR for those languages are limited. Currently, the dominant source language of large-scale datasets is still English, followed by Chinese, Russian, Arabic and a few other European languages. Moreover, researchers in the future could also work on improving databases’ quality from various aspects (Xia et al., 2020). To name a few, build publicly easily accessible databases for general purposes, build databases that have multiple recording angles, and record audio and video under high quality.
With regard to multimodal, it may be possible that future research does not be limited to bimodal, i.e. visual and audio; it could be a trimodal, or even a true multimodal as “sight - listening - touching - tasting - smelling” (Xia et al., 2020). One previous research has already suggested a trimodal including audio, visual and aero-tactile information for speech perception under a noisy background (Derrick et al., 2019). In this study, air puff was added to audiovisual stimuli /pa/ and /ba/ and the matched pairs (e.g. /pa/ with air puff) had higher speech clarity than mismatched pairs, represented by the decreased SNR that listeners needed for matched pairs. With the ability of machine learning and deep learning, it might just be a matter of time to discover how to extract, represent and fuse multimodal features to current speech recognition modal.
What’s more, researchers could explore if multimodal speech recognition could be applied to a wider domain. It is classic that automatic speech recognition is used for healthcare purposes such as transcribing clinical notes by Nuance and recognizing whisper speech from patients by Whispp, but with the help of an extra modality i.e. visual information, it seems new to use audiovisual speech recognition in forensic fields. A good case would be that an audiovisual speech recognition was used to transcribe audio-visual speech materials and detect child abuse (Vásquez-Correa & Álvarez Muniain, 2023).
Finally, novel deep learning architectures might also be worth implementing in future research. These could be new modals that better integrate features among different modalities and thus could improve the performance of speech recognition. For example, the first Hybrid CTC/Attention architecture for audio-visual recognition and this architecture led to a decrease in word error by 1.3% (Petridis et al., 2018). Some new approaches are also mentioned in other studies, for example, integration between DNN-HMM and MSHMM (Noda et al., 2015).
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References
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