Now that we’re back from the  50th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025) in Hyderabad, India, we’d like to take a moment to reflect on new approaches that are reshaping speech technology, signal processing and their integration into new AI technologies.  

Under the conference theme “Celebrating Signal Processing,” researchers and industry leaders shared insights that extend far beyond the published papers, offering a glimpse into the future of AI, particularly generative AI applications, in speech and audio. 

Advancing speech recognition with large language models (LLMs) 

The conference highlighted a fundamental shift in how speech recognition systems are being designed and deployed. Unlike traditional approaches that treat speech recognition as an isolated task, today’s most advanced systems are deeply integrated with large language models (LLMs). 

One noteworthy paper, Efficient Streaming LLM for Speech Recognition, introduced SpeechLLM-XL, a new model that handles streaming speech recognition with unprecedented efficiency. This Best Industry Paper Award winner from Microsoft researchers highlighted a model that processes audio in configurable chunks with limited attention windows for reduced computation. What makes this advancement of interest is its ability to handle long form utterances 10x longer than the training data. 

The convergence of generative AI and signal processing 

Perhaps the most important insight from ICASSP 2025 was how thoroughly AI and traditional signal processing have converged, creating synergies that neither field could achieve alone. 

In a plenary talk, Dr. Ahmed Tewfik, Machine Learning Director at Apple, presented Bridging Generative AI and Statistical Signal Processing. It emphasized how recent generative models have improved performance by blending mathematical and data driven methods. He highlighted two popular classes: diffusion and structured state space models and explained how late 20th century breakthroughs in statistical signal processing can reduce model size, training and inference time, inspiring novel architectures. 

NVIDIA’s Dr. Jakob Hoydis expanded on this in his keynote about differentiable simulation for communication systems, showing how making every component of a wireless simulation trainable by gradients is spurring new AI driven innovations in 6G radio design. 

The critical role of training data 

Even with architectural innovations, the quality and quantity of training data remain fundamental challenges for advanced speech and audio AI. ICASSP 2025 featured unusually candid discussions about these data challenges. 

Addressing data scarcity 

Despite the success of large datasets in training speech and language models, experts emphasized that high-quality speech data is still scarce for most of the world’s languages. This gap limits the globalization of speech technology and creates disparities in performance across languages. 

Indic languages were front and center in this discussion at ICASSP 2025.  

In the SALMA workshop (Speech and Audio Language Models), Google DeepMind’s Bhuvana Ramabhadran stressed the need for “joint representations that span languages and modalities” and self-supervised methods to leverage unlabeled audio.  

Recent advances like the new IndicST benchmark and datasets such as IndicVoices-R (covering 22 scheduled languages with 1,704 hours) and Bhashini “Vaani” (58 language variants) are beginning to address this gap, though researchers noted that most of India’s 121 living languages remain “low resource” by global ASR standards. 

The consensus trend is shifting toward language-family pooling (Indo-Aryan vs. Dravidian) and smart fine-tuning rather than monolithic models. For companies seeking to develop robust speech systems for these markets, specialized data providers with access to diverse pools of native Indic language speakers can make the difference between a working product and one that falters in real-world usage. 

Synthetic data: promise and pitfalls 

The GenDA workshop on Generative Data Augmentation showcased how generative models are advancing signal processing. Participants showed generative adversarial networks (GANs), diffusion models, and Transformers being used to synthesize high-fidelity audio data for training models when real data is scarce. Adobe Research’s Oriol Nieto gave a fascinating demo of AI-driven sound design, using latent diffusion models to generate sound effects from text descriptions or even from silent video inputs.  

His examples (like Sketch2Sound for turning drawn sketches into audio, and MultiFoley for generating background sounds for mute video scenes) made a strong case that we’re at the dawn of a new creative paradigm in sound engineering. 

However, workshop participants emphasized that synthetic data comes with significant pitfalls: it can amplify existing biases, create distribution drift between training and real-world conditions, and produce acoustic artifacts that models might incorrectly learn as features. The consensus was that synthetic data serves best as a complement to human-recorded datasets, not a replacement.  

Experts recommended “hybrid sampling” approaches that maintain a significant percentage of real recordings, explicit conditioning on attributes like accent and acoustic environment, and regular evaluation against purely human-recorded test sets. 

The future of speech + audio AI 

ICASSP 2025 made it clear that we’re at an inflection point in speech and audio technology. The integration of LLMs with speech recognition, the convergence of AI and signal processing, and innovative approaches to training data are creating new possibilities for more natural, efficient, and accessible voice interfaces. 

By investing in high quality AI training data and embracing hybrid approaches that combine the best of traditional signal processing with modern AI and gen AI, companies can build speech and audio applications that are more capable, efficient, and accessible than ever before. 

Learn more about LXT AI data solutions here.