Ranking the Impact of Contextual Specialization in Neural Speech Enhancement
2026-07-06 • Sound
Sound
AI summaryⓘ
The authors studied different sizes of speech enhancement models, from very small to medium-large, to see how well they work when tailored to specific sounds or speakers. They found that customizing models for individual speakers improved speech clarity and quality the most, while focusing on noise type or gender helped less. Interestingly, a small model specialized for a specific speaker and noise type performed as well as much larger general models. They also discovered that models focused on a single language beat those designed for multiple languages. This suggests small, adaptable models could be useful in devices like hearing aids by adjusting to the listener’s environment quickly.
speech enhancementneural networksmodel specializationspeaker identitynoise typesignal-to-noise ratio (SNR)cross-lingual testingfine-tuningspeech intelligibilityadaptive models
Authors
Peter Leer, Svend Feldt, Zheng-Hua Tan, Jan Østergaard, Jesper Jensen
Abstract
We systematically investigate neural speech enhancement systems, ranging from very small ($\sim$10\,k parameters) to medium-large ($\sim$2-5\,M parameters), which specialize to acoustic conditions using contextual information such as speaker identity, noise type, speaker gender, spoken language, and SNR. By fine-tuning generalist models on specific data subsets, we find that specializing to a speaker's identity consistently yields the largest gains in estimated speech intelligibility and quality. In contrast, specializing to SNR, noise type, or gender offers only marginal benefits. Crucially, we show that a small model specialized to both a specific speaker and a specific noise type can match or exceed the performance of a generalist model ten times its size. Further, cross-lingual tests reveal that models specialized to a target language outperform multilingual generalists, suggesting that language is a salient feature for specialization. These findings highlight the potential of small, adaptive models for resource-constrained applications like hearing aids, which specialize on-the-fly to contextual information.