Speech and Language Processing

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The ICASSP 2024 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems.

Speech-enabled systems often experience performance degradation in real-world scenarios, primarily due to adverse acoustic conditions and interactions among multiple speakers. Enhancing the front-end speech processing technology is vital for improving the performance of the back-end systems. 

This challenge is the continuation of LIMMITS'23 (ICASSP 23 SPGC), it is aimed at making further progress in multi-speaker, multi-lingual TTS by extending the problem statement to voice cloning.  

As cars become indispensable parts of human daily life, a safe and comfortable driving environment is more desirable. The traditional touch-based interaction in cockpit is easy to distract the drivers' attention, leading to inefficient operations and potential security risks. 

The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems. For example, in the ICASSP 2022 Deep Noise Suppression challenge, the improvement in the background (BAK) and overall (OVRL) quality is impressive, but the improvement in the speech signal (SIG) is statistically zero.

The LIMMITS’23 challenge on LIghtweight, Multi-speaker, Multi-lingual Indic Text-to-Speech Synthesis is being organized as part of the Signal Processing Grand Challenge track at ICASSP 2023. As a part of this challenge, TTS corpora in Marathi, Hindi, and Telugu datasets will be released. These TTS corpora are being built in the SYSPIN project at SPIRE lab, Indian Institute of Science (IISc) Bangalore, India.

The advent of spoken language processing (SLP) technologies on meeting transcripts is crucial for distilling, organizing, and prioritizing information. Meeting transcripts impose two key challenges to SLP tasks.  First, meeting transcripts exhibit a wide variety of spoken language phenomena, leading to dramatic performance degradation.  Second, meeting transcripts are usually long-form documents with several thousand words or more, posing a great challenge to mainstay Transformer-based models with high computational complexity.

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