Neural Target Speech Extraction: An overview

Look Once to Hear: Target Speech Hearing with Noisy Examples

“…Neural networks for target speech extraction. The goal here is to extract the speech signal of a target speaker, from a mixture of several speakers, given additional clues to identify the target speaker [70]. Prior work has explored three kinds of clues: audio clues from pre-recordings of the target speaker [7,18,23,36,67,71,72], visual clues using a video recording [52] and spatial clues by providing the direction and/or location of the target speaker.…”

“…Target speech extraction is also related to the more general blind source separation problem [68] where the task is to separate all speakers in a mixture. This is challenging with an unknown number of speakers and with permutations between mapping the model output to the corresponding speakers [70].…”

“…Our key observation is that for hearable applications of deep learningbased target speech extraction [21,70,72], it is often impractical to obtain a clean speech sample of the target speaker. In this work, we propose a target speech hearing (TSH) system suitable for binaural hearables applications that provides an interface for noisy in-thewild speech samples, which we refer to as noisy enrollments.…”

“…The latter, which we call target speech hearing, is a new capability for general-purpose hearable devices. Existing deep learning approaches for the problem of target speech extraction require prior clean audio examples of the target speaker [70]. These clean examples are utilized by a neural network to learn the characteristics of the target speaker, which are subsequently employed to separate their speech from that of other concurrent speakers.…”

Exploring Advances in Meeting Minutes Generation and Face Attendance Systems: A Comprehensive Literature Survey

“…Despite the progress, notable challenges persist in dynamic scenarios where the target speaker's location is not fixed. Additionally, the paper raises awareness of a gap in research, as investigations into these dynamic cases are relatively rare, emphasizing the need for further exploration in this area to enhance the applicability of TSE methodologies[5].In a certain research work the proposed approach follows a significant trend in using speech recognition for efficient speech-to-text conversion, offering potential benefits in transcription and enhancing content understanding, particularly in fields like lecture note archiving. This model seamlessly integrates speech recognition technology, providing a comprehensive solution for transcribing spoken language.Nonetheless, a notable challenge lies in its limited focus, primarily summarizing sentences that conclude with a full stop or contain brief pauses marked by commas, overlooking other punctuation marks.…”