Microsoft Research | Research intern talk: Unified speech enhancement approach for speech degradation & noise suppression @MicrosoftResearch | Uploaded November 2023 | Updated October 2024, 1 week ago.
Speakers: Khandokar Md. Nayem
Host: Sebastian Braun
Speech enhancement approaches generally focus on removing additive noise and reverberation that adversely affects the overall speech quality and intelligibility. Another group of signal degradations like clipping, bandwidth limitations, and codec degradation can occur due to poor recording hardware, network transmission, and other pre-processing. These degradations largely impact on intelligibility and speech quality. In this work, we deploy a convolutional recurrent network to remove these speech degradations in conjunction with the noise suppression task and propose cascade and end-to-end approaches. We compare both complex mask and direct spectrum estimation approaches for this task using a small real-time capable DNN. Overall, we propose a cascaded processing approach, addressing the distortion types differently, and enabling a task-tailored modular processing.
Learn more: microsoft.com/en-us/research/video/research-intern-talk-unified-speech-enhancement-approach-for-speech-degradations-noise-suppression
Speakers: Khandokar Md. Nayem
Host: Sebastian Braun
Speech enhancement approaches generally focus on removing additive noise and reverberation that adversely affects the overall speech quality and intelligibility. Another group of signal degradations like clipping, bandwidth limitations, and codec degradation can occur due to poor recording hardware, network transmission, and other pre-processing. These degradations largely impact on intelligibility and speech quality. In this work, we deploy a convolutional recurrent network to remove these speech degradations in conjunction with the noise suppression task and propose cascade and end-to-end approaches. We compare both complex mask and direct spectrum estimation approaches for this task using a small real-time capable DNN. Overall, we propose a cascaded processing approach, addressing the distortion types differently, and enabling a task-tailored modular processing.
Learn more: microsoft.com/en-us/research/video/research-intern-talk-unified-speech-enhancement-approach-for-speech-degradations-noise-suppression
![Podcast | Collaborators: Teachable AI with Cecily Morrison & Karolina Pakėnaitė [ASL interpretation]](https://i.ytimg.com/vi/e2Kd0O8Wr-8/hqdefault.jpg "Podcast | Collaborators: Teachable AI with Cecily Morrison & Karolina Pakėnaitė [ASL interpretation]
The Microsoft Research Podcast series Collaborators explores the relationships behind the projects, products, and services being pursued and delivered by researchers at Microsoft and the diverse range of people they’re teaming up with.
In this episode, host Gretchen Huizinga speaks with Cecily Morrison, MBE, a Senior Principal Research Manager at Microsoft Research, and Karolina Pakėnaitė, a PhD student and member of the citizen design team working with Morrison on the research project Find My Things. An AI phone application designed to help people who are blind or have low vision locate their personal items, Find My Things is an example of a broader research approach known as Teachable AI. Morrison and Pakėnaitė explore the Teachable AI goal of empowering people to make AI experiences work for them and discuss how “designing for one” leads to innovative solutions and what they learned about optimizing these systems for real-world use (spoiler: it’s not necessarily more or higher-quality data).
For audio of the episode, a full transcript, and other resources, visit the episode landing page: https://www.microsoft.com/en-us/research/podcast/collaborators-teachable-ai-with-cecily-morrison-and-karolina-pakenaite/")
