Report on Current Developments in Speech Synthesis and Emotion Recognition
General Direction of the Field
The recent advancements in speech synthesis and emotion recognition are pushing the boundaries of what is possible in human-computer interaction and multimedia applications. The field is witnessing a shift towards more nuanced and dynamic emotional expression in synthesized speech, as well as improved robustness in emotion recognition under noisy conditions. Innovations are being driven by the integration of advanced machine learning techniques, such as continuous latent space modeling and crossmodal embeddings, which enable more precise control over emotional intensity and real-time response generation.
One of the key trends is the development of frameworks that allow for fine-grained control over emotional expression in synthesized speech. This is being achieved through novel prompt selection strategies and the use of few-shot learning, which enables the synthesis of diverse emotional nuances with minimal input data. Additionally, there is a growing emphasis on the dynamic nature of emotional expression, where models are being designed to capture and reproduce subtle fluctuations in emotion intensity over time.
In the realm of emotion recognition, there is a significant focus on enhancing robustness in noisy environments, particularly in the presence of human speech noise. This is being addressed through the use of two-stage frameworks that first extract the target speaker's voice from a mixture and then apply emotion recognition techniques. These approaches are showing promising results in improving the accuracy of emotion recognition in challenging conditions.
Another notable development is the real-time generation of listener responses in dyadic interactions. Models are being developed to generate continuous head motion responses that reflect a listener's engagement and emotional state, which is crucial for naturalistic human-robot interactions. These models are data-driven and do not rely on oversimplified representations of head motion, making them more realistic and deployable in real-world scenarios.
Noteworthy Papers
EmoPro: Introduces a two-stage prompt selection strategy for emotionally controllable speech synthesis, significantly enhancing the expressiveness of synthesized speech.
Text2FX: Demonstrates the use of CLAP embeddings for text-guided audio effects, offering a novel approach to controlling audio effects with natural language prompts.
Active Listener: Proposes a graph-based model for real-time generation of listener head motion responses, achieving high accuracy and frame rate in dyadic interactions.
These papers represent significant strides in the field, offering innovative solutions that advance the capabilities of speech synthesis and emotion recognition systems.
