Existing pre-trained models like Distil HuBERT excel at uncovering hidden patterns and facilitating accurate recognition across diverse data types, such as audio and visual information. We harnessed this capability to...Existing pre-trained models like Distil HuBERT excel at uncovering hidden patterns and facilitating accurate recognition across diverse data types, such as audio and visual information. We harnessed this capability to develop a deep learning model that utilizes Distil HuBERT for jointly learning these combined features in speech emotion recognition (SER). Our experiments highlight its distinct advantages: it significantly outperforms Wav2vec 2.0 in both offline and real-time accuracy on RAVDESS and BAVED datasets. Although slightly trailing HuBERT’s offline accuracy, Distil HuBERT shines with comparable performance at a fraction of the model size, making it an ideal choice for resource-constrained environments like mobile devices. This smaller size does come with a slight trade-off: Distil HuBERT achieved notable accuracy in offline evaluation, with 96.33% on the BAVED database and 87.01% on the RAVDESS database. In real-time evaluation, the accuracy decreased to 79.3% on the BAVED database and 77.87% on the RAVDESS database. This decrease is likely a result of the challenges associated with real-time processing, including latency and noise, but still demonstrates strong performance in practical scenarios. Therefore, Distil HuBERT emerges as a compelling choice for SER, especially when prioritizing accuracy over real-time processing. Its compact size further enhances its potential for resource-limited settings, making it a versatile tool for a wide range of applications.展开更多
A key issue for the future direction of film studies is what is the nature of the perception film viewers have of a film and what is the nature of the perception characters in a film have of other characters in the fi...A key issue for the future direction of film studies is what is the nature of the perception film viewers have of a film and what is the nature of the perception characters in a film have of other characters in the film. The debate between the philosophers John McDowell and Hubert Dreyfus over the nature of perception in general illuminates this issue. McDowell argues that we must see perception as conceptual, while Dreyfus supports a non-conceptual view. McDowell＇s concept of second nature not only resolves this debate in his favor, but it provides a promising tool for the interpretation of individual films. Moreover, McDowell＇s conceptual view of perception rules out those approaches to the future of film studies that are based on a non-conceptualist framework. Finally, McDowell＇s approach leads to an emphasis on interpreting films with a focus on improving moral sensibilities. This perspective provides a viable blueprint for keeping film studies viable.展开更多
文摘Existing pre-trained models like Distil HuBERT excel at uncovering hidden patterns and facilitating accurate recognition across diverse data types, such as audio and visual information. We harnessed this capability to develop a deep learning model that utilizes Distil HuBERT for jointly learning these combined features in speech emotion recognition (SER). Our experiments highlight its distinct advantages: it significantly outperforms Wav2vec 2.0 in both offline and real-time accuracy on RAVDESS and BAVED datasets. Although slightly trailing HuBERT’s offline accuracy, Distil HuBERT shines with comparable performance at a fraction of the model size, making it an ideal choice for resource-constrained environments like mobile devices. This smaller size does come with a slight trade-off: Distil HuBERT achieved notable accuracy in offline evaluation, with 96.33% on the BAVED database and 87.01% on the RAVDESS database. In real-time evaluation, the accuracy decreased to 79.3% on the BAVED database and 77.87% on the RAVDESS database. This decrease is likely a result of the challenges associated with real-time processing, including latency and noise, but still demonstrates strong performance in practical scenarios. Therefore, Distil HuBERT emerges as a compelling choice for SER, especially when prioritizing accuracy over real-time processing. Its compact size further enhances its potential for resource-limited settings, making it a versatile tool for a wide range of applications.
文摘A key issue for the future direction of film studies is what is the nature of the perception film viewers have of a film and what is the nature of the perception characters in a film have of other characters in the film. The debate between the philosophers John McDowell and Hubert Dreyfus over the nature of perception in general illuminates this issue. McDowell argues that we must see perception as conceptual, while Dreyfus supports a non-conceptual view. McDowell＇s concept of second nature not only resolves this debate in his favor, but it provides a promising tool for the interpretation of individual films. Moreover, McDowell＇s conceptual view of perception rules out those approaches to the future of film studies that are based on a non-conceptualist framework. Finally, McDowell＇s approach leads to an emphasis on interpreting films with a focus on improving moral sensibilities. This perspective provides a viable blueprint for keeping film studies viable.
