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Recent advancements in speech processing are increasingly focused on enhancing the effectiveness and efficiency of speech recognition and extraction systems, particularly in real-time applications. Researchers are developing frameworks that allow for robust target speaker extraction from overlapping speech, addressing the challenges posed by real-world audio environments. Techniques like the Chunk-wise Interleaved Splicing Paradigm and the two-stage Mask2Flow-TSE approach are demonstrating significant improvements in extraction fidelity and latency, making them suitable for consumer-level applications. Additionally, the emergence of multilingual benchmarks for phoneme discovery and unified speech encoders is fostering a deeper understanding of language-specific nuances, which could streamline the development of language-agnostic tools. The integration of zero-shot voice style conversion systems also highlights a growing interest in personalizing speech applications, potentially transforming user interactions across various platforms. Collectively, these efforts indicate a shift towards more adaptable, efficient, and user-friendly speech technologies that can meet diverse commercial needs.
We introduce DiscoPhon, a multilingual benchmark for evaluating unsupervised phoneme discovery from discrete speech units. DiscoPhon covers 6 dev and 6 test languages, chosen to span a wide range of p...
Speech foundation models trained with self-supervised learning produce generic speech representations that support a wide range of speech processing tasks. When further adapted with supervised learnin...
Target speech extraction (TSE) aims to recover a target speaker's voice from a mixture. While recent text-prompted approaches have shown promise, most approaches assume fully overlapped mixtures, limi...
While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in real-time applications. Direct adaptation to stre...
Voice style conversion aims to transform an input utterance to match a target speaker's timbre, accent, and emotion, with a central challenge being the disentanglement of linguistic content from style...
Syllable-level units offer compact and linguistically meaningful representations for spoken language modeling and unsupervised word discovery, but research on syllabification remains fragmented across...
Target speaker extraction (TSE) extracts the target speaker's voice from overlapping speech mixtures given a reference utterance. Existing approaches typically fall into two categories: discriminative...
We propose to model parallel streams of data, such as overlapped speech, using shuffles. Specifically, this paper shows how the shuffle product and partial order finite-state automata (FSAs) can be us...
Text-speech joint spoken language modeling (SLM) aims at natural and intelligent speech-based interactions, but developing such a system may suffer from modality mismatch: speech unit sequences are mu...
Current speech LLMs largely perform implicit ASR: on tasks solvable from a transcript, they are behaviorally and mechanistically equivalent to simple Whisper$\to$LLM cascades. We show this through mat...
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Canonical ID speech-processing | Route /topic/speech-processing
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}Use This Via API or MCP
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