In a notable step forward in AI, a new model called ‘SpeechSSM’ is carving a path for continuous, natural speech generation. Developed by Sejin Park, a PhD candidate at the Korea Advanced Institute of Science and Technology (KAIST), this innovation tackles the challenge of keeping long-duration speech both coherent and engaging.
If you’ve ever wrestled with voice systems that lose track over time—be it in podcasts, audiobooks or virtual assistants—you’ll appreciate how this model improves the experience. Traditional spoken language models (SLMs) can stumble when generating extensive speech, but by blending attention layers (which focus on the latest details) with recurrent layers (that maintain the broader narrative), SpeechSSM manages to keep the conversation flowing naturally.
The model also employs a smart windowing strategy. It processes speech data in small, manageable chunks before combining them, which stabilises memory usage and lightens the computational load. This means you get high-quality, continuous speech that sounds as natural as a face-to-face conversation.
Available on the arXiv preprint server and set to be showcased at the International Conference on Machine Learning (ICML) 2025, the research introduces new evaluation metrics – SC-L for semantic coherence and N-MOS-T for naturalness. As Se Jin Park explains, “Our goal was to create a spoken language model that could generate long-duration speech truly fit for everyday human interaction.”
Working in collaboration with Google DeepMind, this development is poised to enhance a range of voice applications, offering more consistent content delivery and real-time responsiveness. For anyone developing voice technology, SpeechSSM represents a well-timed solution to one of the field’s persistent challenges.
