Linear regression models have a wide range of applications in statistics, signal processing, and machine learning. In this Lecture Notes column we will examine the performance of the least-squares (LS) estimator with a focus on the case when there are more parameters than training samples, which is often overlooked in textbooks on estimation.
Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in 9,000+ h of data. It is essential to identify each speaker’s role during Apollo and analyze group communication to achieve a common goal.
The underrepresentation of women in science, technology, engineering, and mathematics (STEM) fields is an issue that has been studied extensively [1] . Yet women still face many challenges, even though the demand for many STEM occupations has exploded. Many factors contribute to the low number of women in the STEM field. From an early age, girls are exposed to many cultural cues that dissuade them from participating in STEM fields. This gender bias is enforced by implicit or explicit messages from multiple sources.
The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic latency to 20ms, and including a full-band version of AECMOS.
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.
In wireless communications, the pathloss (or large scale fading coefficient) quantifies the loss of signal strength between a transmitter (Tx) and a receiver (Rx) due to large scale effects, such as free-space propagation loss, and interactions of the radio waves with the obstacles (which block line-of sight, like buildings, vehicles, pedestrians), e.g. penetrations, reflections and diffractions.
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.
The MADReSS SPGC targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer’s Dementia (AD). Dementia is a category of neurodegenerative diseases that entails a long-term and usually gradual decrease of cognitive functioning.
Spoken Language Understanding (SLU) is a critical component of conversational voice assistants, requiring converting user utterances into a structured format for task executions. SLU systems typically consist of an ASR component to convert audio to text and an NLU component to convert text to a tree like structure, however recently, E2E SLU systems have also become of increasing interest in order to increase quality, model efficiency, and data efficiency.
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